Machine Learning Group, TU Berlin - Publications

Note that the PDFs available on this web page are the authors' draft versions of the respective papers. The authoritative versions must be retrieved from the publisher.

2023

Journal papers

L. Andeol, Y. Kawakami, Y. Wada, T. Kanamori, K. Müller, G. Montavon, Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization
Neural Networks, 167:233-243, 2023 [bibtex]

S. Bacchio, P. Kessel, S. Schaefer, L. Vaitl, Learning trivializing gradient flows for lattice gauge theories
Physical Review D, American Physical Society (APS), 107(5), 2023 [bibtex] [url]

A. Bauer, S. Nakajima, K. Müller, Polynomial-Time Constrained Message Passing for Exact MAP Inference on Discrete Models with Global Dependencies
Mathematics, 11(12), 2023 [bibtex] [url]

S. Blücher, K. Müller, S. Chmiela, Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence
Journal of Chemical Theory and Computation, 19(14):4619–4630, 2023 [bibtex]

P. Keyl, P. Bischoff, G. Dernbach, M. Bockmayr, R. Fritz, D. Horst, N. Blüthgen, G. Montavon, K. Müller, F. Klauschen, Single-cell gene regulatory network prediction by explainable AI
Nucleic Acids Research, Oxford University Press (OUP), 2023 [bibtex] [url]

M. F. Langer, F. Knoop, C. Carbogno, M. Scheffler, M. Rupp, Heat flux for semilocal machine-learning potentials
Physical Review B, American Physical Society, 108(10):L100302, 2023 [bibtex]

J. Lederer, M. Gastegger, K. T. Schütt, M. Kampffmeyer, K. Müller, O. T. Unke, Automatic identification of chemical moieties
Physical Chemistry Chemical Physics, Royal Society of Chemistry, 2023 [bibtex]

S. Letzgus, K. Müller, Towards transparent and robust data-driven wind turbine power curve models
2023 [bibtex]

L. Linhardt, K. Müller, G. Montavon, Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks
2023 [bibtex]

L. Muttenthaler, R. A. Vandermeulen, T. Unterthiner, K. Müller, others, Set Learning for Accurate and Calibrated Models
2023 [bibtex]

L. Muttenthaler, L. Linhardt, J. Dippel, R. A. Vandermeulen, K. Hermann, A. K. Lampinen, S. Kornblith, Improving neural network representations using human similarity judgments
2023 [bibtex]

K. T. Schütt, S. S. Hessmann, N. W. Gebauer, J. Lederer, M. Gastegger, SchNetPack 2.0: A neural network toolbox for atomistic machine learning
The Journal of Chemical Physics, 158(14):144801, 2023 [bibtex] [url]

A. Streck, T. L. Kaufmann, R. F. Schwarz, SMITH: spatially constrained stochastic model for simulation of intra-tumour heterogeneity
Bioinformatics, 39(3), 2023 [bibtex]

J. Vielhaben, S. Lapuschkin, G. Montavon, W. Samek, Explainable AI for Time Series via Virtual Inspection Layers
2023 [bibtex]

J. Vielhaben, S. Blücher, N. Strodthoff, Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees,
Transactions on Machine Learning Research, 2023 [bibtex] [url]

D. Wagner, T. Michels, F. C. Schulz, A. Nair, M. Rudolph, M. Kloft, TimeSeAD: Benchmarking Deep Multivariate Time-Series Anomaly Detection,
Transactions on Machine Learning Research, 2023 [bibtex] [url]

Conference papers

A. Binder, L. Weber, S. Lapuschkin, G. Montavon, K. Müller, W. Samek, Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations
CVPR, IEEE, 2023 [bibtex]

L. Muttenthaler, J. Dippel, L. Linhardt, R. A. Vandermeulen, S. Kornblith, Human alignment of neural network representations
The Eleventh International Conference on Learning Representations, 2023 [bibtex] [url]

P. Xiong, T. Schnake, M. Gastegger, G. Montavon, K. Müller, S. Nakajima, Relevant Walk Search for Explaining Graph Neural Networks
International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, PMLR, Proceedings of Machine Learning Research, 202:38301-38324, 2023 [bibtex] [url]

2022

Journal papers

C. J. Anders, L. Weber, D. Neumann, W. Samek, K. Müller, S. Lapuschkin, Finding and removing Clever Hans: Using explanation methods to debug and improve deep models
Inf. Fusion, 77:261-295, 2022 [bibtex] [url]

S. Barber, L. A. Lima, Y. Sakagami, J. Quick, E. Latiffianti, Y. Liu, R. Ferrari, S. Letzgus, X. Zhang, F. Hammer, Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study
Energies, Multidisciplinary Digital Publishing Institute, 15(15):5638, 2022 [bibtex]

S. Blücher, J. Vielhaben, N. Strodthoff, PredDiff: Explanations and interactions from conditional expectations
Artificial Intelligence, 312:103774, 2022 [bibtex]

P. Chormai, Y. Pu, H. Hu, S. E. Fisher, C. Francks, X. Kong, Machine learning of large-scale multimodal brain imaging data reveals neural correlates of hand preference
NeuroImage, 262:119534, 2022 [bibtex] [url]

P. Chormai, J. Herrmann, K. Müller, G. Montavon, Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces
arXiv, 2022 [bibtex] [url]

I. Daniel, J. Pesantez, S. Letzgus, M. A. Khaksar Fasaee, F. Alghamdi, E. Berglund, G. Mahinthakumar, A. Cominola, A Sequential Pressure-Based Algorithm for Data-Driven Leakage Identification and Model-Based Localization in Water Distribution Networks
Journal of Water Resources Planning and Management, American Society of Civil Engineers, 148(6):04022025, 2022 [bibtex]

A. Dombrowski, C. J. Anders, K. Müller, P. Kessel, Towards robust explanations for deep neural networks
Pattern Recognition, Elsevier, 121:108194, 2022 [bibtex]

N. W. Gebauer, M. Gastegger, S. S. Hessmann, K. Müller, K. T. Schütt, Inverse design of 3d molecular structures with conditional generative neural networks
Nature Communications, 13(1):973, 2022 [bibtex] [url]

T. L. Kaufmann, M. Petkovic, T. B. Watkins, E. C. Colliver, S. Laskina, N. Thapa, D. C. Minussi, N. Navin, C. Swanton, P. Van Loo, K. Haase, M. Tarabichi, R. F. Schwarz, MEDICC2: Whole-Genome Doubling Aware Copy-Number Phylogenies for Cancer Evolution
Genome Biology, 23(1):241, 2022 [bibtex]

P. Keyl, M. Bockmayr, D. Heim, G. Dernbach, G. Montavon, K. Müller, F. Klauschen, Patient-level proteomic network prediction by explainable artificial intelligence
npj Precision Oncology, Springer Science and Business Media LLC, 6(1):35, 2022 [bibtex] [url]

S. L. Krug, G. F. v. Rudorff, O. A. v. Lilienfeld, Relative energies without electronic perturbations via alchemical integral transform
The Journal of Chemical Physics, 157(16):164109, 2022 [bibtex] [url]

M. F. Langer, A. Goessmann, M. Rupp, Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
npj Computational Materials, 8(41), 2022 [bibtex]

S. Letzgus, P. Wagner, J. Lederer, W. Samek, K. Müller, G. Montavon, Toward Explainable Artificial Intelligence for Regression Models: A methodological perspective
IEEE Signal Processing Magazine, 39(4):40-58, 2022 [bibtex]

P. Liznerski, L. Ruff, R. A. Vandermeulen, B. J. Franks, K. R. Müller, M. Kloft, Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images,
Transactions on Machine Learning Research, 2022 [bibtex] [url]

A. Ortega-Martinez, A. Von Lühmann, P. Farzam, D. Rogers, E. M. Mugler, D. A. Boas, M. A. Yücel, Multivariate Kalman filter regression of confounding physiological signals for real-time classification of fNIRS data
Neurophotonics, 9(02), 2022 [bibtex] [url]

H. E. Sauceda, L. E. Gálvez-González, S. Chmiela, L. O. Paz-Borbón, K. Müller, A. Tkatchenko, BIGDML-Towards accurate quantum machine learning force fields for materials
Nature Communications, 13(1):3733, 2022 [bibtex] [url]

N. F. Schmitz, K. Müller, S. Chmiela, Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields
The Journal of Physical Chemistry Letters, 13(43):10183-10189, 2022 [bibtex] [url]

T. Schnake, O. Eberle, J. Lederer, S. Nakajima, K. T. Schütt, K. Müller, G. Montavon, Higher-Order Explanations of Graph Neural Networks via Relevant Walks
IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11):7581-7596, 2022 [bibtex]

S. Tucker, J. Dubb, S. Kura, A. v. Lühmann, R. Franke, J. M. Horschig, S. Powell, R. Oostenveld, M. Lührs, É. Delaire, Z. M. Aghajan, H. Yun, M. A. Yücel, Q. Fang, T. J. Huppert, B. B. Frederick, L. Pollonini, D. Boas, R. Luke, Introduction to the shared near infrared spectroscopy format
Neurophotonics, 10(01), 2022 [bibtex] [url]

L. Vaitl, K. A. Nicoli, S. Nakajima, P. Kessel, Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL divergence for Normalizing Flows
Machine Learning: Science and Technology, 2022 [bibtex] [url]

L. Winkler, K. Müller, H. E. Sauceda, High-fidelity molecular dynamics trajectory reconstruction with bi-directional neural networks
Machine Learning: Science and Technology, 3(2):025011, 2022 [bibtex] [url]

Conference papers

A. Ali, T. Schnake, O. Eberle, G. Montavon, K. Müller, L. Wolf, XAI for Transformers: Better Explanations through Conservative Propagation
Proceedings of the 39th International Conference on Machine Learning, PMLR, Proceedings of Machine Learning Research, 162:435-451, 2022 [bibtex]

D. Arp, E. Quiring, F. Pendlebury, A. Warnecke, F. Pierazzi, C. Wressnegger, L. Cavallaro, K. Rieck, Dos and Don'ts of Machine Learning in Computer Security
Proc. of the USENIX Security Symposium, 2022 [bibtex] [url]

C. Bartkowski, A. Nandori, A. v. Lühmann, C. Schmitz, Towards a fully integrated Smart Textile patch-based cap for multi-distance CW fNIRS whole-head imaging
Proc. Biennial Meeting of the Society for fNIRS 2022, SfNIRS, 2022 [bibtex]

E. J. Braun, E. Carpenter, Y. Gao, A. Cronin-Golomb, T. Ellis, D. C. Somers, A. v. Lühmann, M. Yücel, D. A. Boas, S. Kiran, Neuroscience in the everyday world: Brain correlates of naturalistic discourse in individuals with aphasia
Proc. Biennial Meeting of the Society for fNIRS 2022, SfNIRS, 2022 [bibtex]

K. Bykov, A. Hedström, S. Nakajima, M. Höhne, NoiseGrad-Enhancing Explanations by Introducing Stochasticity to Model Weights
Proceedings of Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI2022), 2022 [bibtex]

S. Czybik, D. Arp, K. Rieck, Quantifying the Risk of Wormhole Attacks on Bluetooth Contact Tracing
CODASPY '22: Twelveth ACM Conference on Data and Application Security and Privacy, Baltimore, MD, USA, April 24 - 27, 2022, ACM, 2022 [bibtex] [url]

Y. Gao, D. Rogers, A. v. Lühmann, A. Ortega-Martinez, D. A. Boas, M. Yücel, Short-separation Regression Incorporated Diffuse Optical Tomography (SS-DOT)
Proc. Biennial Meeting of the Society for fNIRS 2022, SfNIRS, 2022 [bibtex]

J. Gerken, O. Carlsson, H. Linander, F. Ohlsson, C. Petersson, D. Persson, Equivariance versus Augmentation for Spherical Images
Proceedings of the 39th International Conference on Machine Learning, PMLR, Proceedings of Machine Learning Research, 162:7404-7421, 2022 [bibtex] [pdf] [url]

A. v. Lühmann, Y. Gao, S. Kura, B. Zimmermann, M. Yücel, D. A. Boas, Can the fNIRS community design a standard cap layout for uniform whole-head HD fNIRS coverage? A discussion
Proc. Biennial Meeting of the Society for fNIRS 2022, SfNIRS, 2022 [bibtex]

A. v. Lühmann, A. Blickensdörfer, M. Kaffes, C. Schmitz, H. Audebert, Exploration of whole-head CW fNIRS-based intracranial hemorrhage detection: progress and challenges
Proc. Biennial Meeting of the Society for fNIRS 2022, SfNIRS, 2022 [bibtex]

L. Muttenthaler, C. Y. Zheng, P. McClure, R. A. Vandermeulen, M. N. Hebart, F. Pereira, VICE: Variational Interpretable Concept Embeddings
Advances in Neural Information Processing Systems, Curran Associates, Inc., 35:33661-33675, 2022 [bibtex] [url]

A. Ortega-Martinez, A. v. Lühmann, D. A. Boas, M. A. Yücel, Closed Loop Feedback fNIRS Brain Computer Interface for Increasing Classification Accuracy in a Left Versus Right Hand Movement Task
Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN), Optica Publishing Group, 2022 [bibtex] [url]

W. J. O’Brien, B. Zimmerman, A. Martinez, R. Bing, P. Farzam, D. Rogers, A. v. Lühmann, D. Boas, NinjaNIRS 2021: Continued Progress towards Whole Head, High Density fNIRS
Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN), Optica Publishing Group, 2022 [bibtex] [url]

W. J. O’Brien, A. Ortega-Martinez, D. Rogers, M. A. Yücel, A. v. Lühmann, S. Kiran, A. Cronin-Golomb, T. Ellis, D. A. Boas, B. Zimmermann, NinjaNIRS 2022: Whole-Head, High-Density Wearable fNIRS with EEG Co-Localization
Proc. Biennial Meeting of the Society for fNIRS 2022, SfNIRS, 2022 [bibtex]

D. Rogers, Y. Gao, D. A. Boas, A. Cronin-Golomb, T. D. Ellis, S. Kiran, D. C. Somers, A. v. Lühmann, M. Yücel, Fast and slow movement-related artifacts in fNIRS signal: what is a viable solution?
Proc. Biennial Meeting of the Society for fNIRS 2022, SfNIRS, 2022 [bibtex]

L. Vaitl, K. A. Nicoli, S. Nakajima, P. Kessel, Path-Gradient Estimators for Continuous Normalizing Flows
Proceedings of the 39th International Conference on Machine Learning, PMLR, Proceedings of Machine Learning Research, 162:21945-21959, 2022 [bibtex] [pdf] [url]

V. Wesselkamp, K. Rieck, D. Arp, E. Quiring, Misleading Deep-Fake Detection with GAN Fingerprints
43rd IEEE Security and Privacy, SP Workshops 2022, San Francisco, CA, USA, May 22-26, 2022, IEEE, 2022 [bibtex] [url]

P. Xiong, T. Schnake, G. Montavon, K. Müller, S. Nakajima, Efficient Computation of Higher-Order Subgraph Attribution via Message Passing
Proceedings of the 39th International Conference on Machine Learning, PMLR, Proceedings of Machine Learning Research, 162:24478-24495, 2022 [bibtex]

L. Yang, D. Grosenick, H. Wabnitz, A. v. Lühmann, Towards the integration of CW fNIRS and absolute oximetry: A proof of concept
Proc. Biennial Meeting of the Society for fNIRS 2022, SfNIRS, 2022 [bibtex]

2021

Journal papers

S. Agarwal, N. Tosi, P. Kessel, S. Padovan, D. Breuer, G. Montavon, Toward Constraining Mars' Thermal Evolution Using Machine Learning
American Geophysical Union (AGU), 8(4), 2021 [bibtex] [url]

C. J. Anders, D. Neumann, W. Samek, K. Müller, S. Lapuschkin, Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy
CoRR, abs/2106.13200, 2021 [bibtex] [url]

A. Binder, M. Bockmayr, M. Hägele, S. Wienert, D. Heim, K. Hellweg, M. Ishii, A. Stenzinger, A. Hocke, C. Denkert, others, Morphological and molecular breast cancer profiling through explainable machine learning
Nature Machine Intelligence, Nature Publishing Group, 3(4):355-366, 2021 [bibtex]

M. Bogojeski, S. Sauer, F. Horn, K. Müller, Forecasting industrial aging processes with machine learning methods
Computers & Chemical Engineering, Elsevier, 144:107123, 2021 [bibtex]

C. Cai, A. Hashemi, M. Diwakar, S. Haufe, K. Sekihara, S. S. Nagarajan, Robust estimation of noise for electromagnetic brain imaging with the Champagne algorithm
NeuroImage, Elsevier, 225:117411, 2021 [bibtex]

M. Gastegger, K. T. Schütt, K. Müller, Machine learning of solvent effects on molecular spectra and reactions
Chemical science, Royal Society of Chemistry, 12(34):11473-11483, 2021 [bibtex]

A. Hashemi, C. Cai, G. Kutyniok, K. Müller, S. Nagarajan, S. Haufe, Unification of Sparse Bayesian Learning Algorithms for Electromagnetic Brain Imaging with the Majorization Minimization Framework
NeuroImage, 239:118309, 2021 [bibtex]

R. P. Joshi, N. W. Gebauer, M. Bontha, M. Khazaieli, R. M. James, J. B. Brown, N. Kumar, 3D-Scaffold: A Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired Scaffolds
J. Phys. Chem. B, 125(44):12166-12176, 2021 [bibtex]

A. v. Lühmann, Y. Zheng, A. Ortega-Martinez, S. Kiran, D. C. Somers, A. Cronin-Golomb, L. N. Awad, T. D. Ellis, D. A. Boas, M. A. Yücel, Toward Neuroscience of the Everyday World (NEW) using functional near-infrared spectroscopy
Current Opinion in Biomedical Engineering, 18:100272, 2021 [bibtex] [url]

D. Lassner, J. Coburger, C. Neudecker, A. Baillot, Publishing an OCR ground truth data set for reuse in an unclear copyright setting. Two case studies with legal and technical solutions to enable a collective OCR ground truth data set effort
Zeitschrift für digitale Geisteswissenschaften, Special issue 5, 2021 [bibtex] [url]

D. Lassner, A. Baillot, S. Dogadov, K. Müller, S. Nakajima, Automatic Identification of Types of Alterations in Historical Manuscripts
Digital Humanities Quarterly, 15(2), 2021 [bibtex] [url]

M. Leitheiser, D. Capper, P. Seegerer, A. Lehmann, U. Schüller, K. Müller, F. Klauschen, P. Jurmeister, M. Bockmayr, Machine Learning Models Predict the Primary Sites of Head and Neck Squamous Cell Carcinoma Metastases Based on DNA Methylation
The Journal of pathology, Wiley Online Library, 2021 [bibtex]

L. Muttenthaler, M. N. Hebart, THINGSvision: A Python Toolbox for Streamlining the Extraction of Activations From Deep Neural Networks
Frontiers in Neuroinformatics, 15:45, 2021 [bibtex] [url]

K. A. Nicoli, C. J. Anders, L. Funcke, T. Hartung, K. Jansen, P. Kessel, S. Nakajima, P. Stornati, Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
Physical review letters, APS, 126(3):032001, 2021 [bibtex]

J. N. Pedroza-Montero, I. L. Garzón, H. E. Sauceda, On the forbidden graphene's ZO (out-of-plane optic) phononic band-analog vibrational modes in fullerenes
Communications Chemistry, 4(1):103, 2021 [bibtex] [url]

L. Ruff, J. R. Kauffmann, R. A. Vandermeulen, G. Montavon, W. Samek, M. Kloft, T. G. Dietterich, K. Müller, A Unifying Review of Deep and Shallow Anomaly Detection
Proceedings of the IEEE, 109(5):756-795, 2021 [bibtex]

W. Samek, G. Montavon, S. Lapuschkin, C. J. Anders, K. Müller, Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
Proc. IEEE, 109(3):247-278, 2021 [bibtex]

H. E. Sauceda, V. Vassilev-Galindo, S. Chmiela, K. Müller, A. Tkatchenko, Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature
Nature Communications, 12(1):442, 2021 [bibtex] [url]

S. R. Soekadar, S. H. Kohl, M. Mihara, A. v. Lühmann, Optical brain imaging and its application to neurofeedback
NeuroImage: Clinical, 30:102577, 2021 [bibtex] [url]

V. Srinivasan, C. Rohrer, A. Marban, K. Müller, W. Samek, S. Nakajima, Robustifying Models Against Adversarial Attacks by Langevin Dynamics
Neural Networks, 137:1-17, 2021 [bibtex]

O. T. Unke, S. Chmiela, H. E. Sauceda, M. Gastegger, I. Poltavsky, K. T. Schütt, A. Tkatchenko, K. Müller, Machine Learning Force Fields
Chemical Reviews, 121(16):10142-10186, 2021 [bibtex] [url]

O. T. Unke, S. Chmiela, M. Gastegger, K. T. Schütt, H. E. Sauceda, K. Müller, SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
Nature Communications, 12(1):7273, 2021 [bibtex] [url]

J. Westermayr, M. Gastegger, K. T. Schütt, R. J. Maurer, Perspective on integrating machine learning into computational chemistry and materials science
The Journal of Chemical Physics, AIP Publishing LLC, 154(23):230903, 2021 [bibtex]

J. Wolff, T. Klein, M. Nabi, R. G. Krishnan, S. Nakajima, Mixture-of-experts VAEs can disregard variation in surjective multimodal data
NeurIPS, Bayesian deep learning workshop, 2021 [bibtex] [url]

M. A. Yücel, A. v. Lühmann, F. Scholkmann, J. Gervain, I. Dan, H. Ayaz, D. Boas, R. J. Cooper, J. Culver, C. E. Elwell, A. Eggebrecht, M. A. Franceschini, C. Grova, F. Homae, F. Lesage, H. Obrig, I. Tachtsidis, S. Tak, Y. Tong, A. Torricelli, H. Wabnitz, M. Wolf, Best practices for fNIRS publications
Neurophotonics, 8(01), 2021 [bibtex] [url]

S. Yeom, P. Seegerer, S. Lapuschkin, A. Binder, S. Wiedemann, K. Müller, W. Samek, Pruning by explaining: A novel criterion for deep neural network pruning
Pattern Recognition, Elsevier, 115:107899, 2021 [bibtex]

Conference papers

L. Deecke, L. Ruff, R. A. Vandermeulen, H. Bilen, Transfer-Based Semantic Anomaly Detection
Proceedings of the 38th International Conference on Machine Learning, PMLR, Proceedings of Machine Learning Research, 139:2546-2558, 2021 [bibtex] [pdf] [url]

A. Dombrowski, J. E. Gerken, P. Kessel, Diffeomorphic Explanations with Normalizing Flows
ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 2021 [bibtex]

A. Hashemi, Y. Gao, C. Cai, S. Ghosh, K. R. Müller, S. S. Nagarajan, S. Haufe, Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging
Thirty-Fifth Conference on Neural Information Processing Systems, 2021 [bibtex] [url]

J. M. Hilgefort, D. Arp, K. Rieck, Spying through Virtual Backgrounds of Video Calls
AISec@CCS 2021: Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security, Virtual Event, Republic of Korea, 15 November 2021, ACM, 2021 [bibtex] [url]

P. Liznerski, L. Ruff, R. A. Vandermeulen, B. J. Franks, M. Kloft, K. R. Müller, Explainable Deep One-Class Classification
International Conference on Learning Representations, 2021 [bibtex] [url]

H. Marienwald, J. Fermanian, G. Blanchard, High-Dimensional Multi-Task Averaging and Application to Kernel Mean Embedding
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR, Proceedings of Machine Learning Research, 130:1963-1971, 2021 [bibtex] [url]

K. A. Nicoli, C. J. Anders, L. Funcke, T. Hartung, K. Jansen, P. Kessel, S. Nakajima, P. Stornati, Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse
The 38th International Symposium on Lattice Field Theory (LATTICE2021), 2021 [bibtex]

A. Ortega-Martinez, A. v. Lühmann, M. A. Yücel, P. Farzam, D. Rogers, D. A. Boas, Real-time regression and classification of functional near infrared spectroscopy signals acquired during motor tasks
Optical Techniques in Neurosurgery, Neurophotonics, and Optogenetics, SPIE, 2021 [bibtex] [url]

K. Schütt, O. Unke, M. Gastegger, Equivariant message passing for the prediction of tensorial properties and molecular spectra
Proceedings of the 38th International Conference on Machine Learning, PMLR, Proceedings of Machine Learning Research, 139:9377-9388, 2021 [bibtex] [pdf] [url]

R. A. Vandermeulen, A. Ledent, Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density Estimation
Advances in Neural Information Processing Systems, 2021 [bibtex] [url]

S. Varshneya, A. Ledent, R. A. Vandermeulen, Y. Lei, M. Enders, D. Borth, M. Kloft, Learning Interpretable Concept Groups in CNNs, Main Track
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, International Joint Conferences on Artificial Intelligence Organization, 2021 [bibtex] [url]

2020

Journal papers

S. Agarwal, N. Tosi, D. Breuer, S. Padovan, P. Kessel, G. Montavon, A machine-learning-based surrogate model of Mars' thermal evolution
Oxford University Press (OUP), 222(3):1656-1670, 2020 [bibtex] [url]

A. Bauer, S. Nakajima, N. Görnitz, K. Müller, Optimizing for Measure of Performance in Max-Margin Parsing
IEEE Transactions on Neural Networks and Learning Systems, 31(7):2680-2684, 2020 [bibtex]

P. Baumeister, S. Padovan, N. Tosi, G. Montavon, N. Nettelmann, J. MacKenzie, M. Godolt, Machine-learning Inference of the Interior Structure of Low-mass Exoplanets
American Astronomical Society, 889(1):42, 2020 [bibtex] [url]

M. Bogojeski, L. Vogt-Maranto, M. E. Tuckerman, K. Müller, K. Burke, Quantum chemical accuracy from density functional approximations via machine learning
Nature communications, Nature Publishing Group, 11(1):1-11, 2020 [bibtex]

S. Brandl, B. Blankertz, Motor Imagery under Distraction - An open access BCI dataset, Open Access
frontneurosci, Frontiers, 14:967, 2020 [bibtex] [url]

O. Eberle, J. Büttner, F. Kräutli, K. Müller, M. Valleriani, G. Montavon, Building and Interpreting Deep Similarity Models
IEEE Transactions on Pattern Analysis and Machine Intelligence, ():1-1, 2020 [bibtex]

M. Gastegger, A. McSloy, M. Luya, K. Schütt, R. Maurer, A deep neural network for molecular wave functions in quasi-atomic minimal basis representation
The Journal of Chemical Physics, AIP Publishing LLC, 153(4):044123, 2020 [bibtex]

M. Hägele, P. Seegerer, S. Lapuschkin, M. Bockmayr, W. Samek, F. Klauschen, K. Müller, A. Binder, Resolving challenges in deep learning-based analyses of histopathological images using explanation methods
Scientific reports, Nature Publishing Group, 10(1):1-12, 2020 [bibtex]

J. Kauffmann, K. Müller, G. Montavon, Towards explaining anomalies: A deep Taylor decomposition of one-class models
Pattern Recognition, 101:107198, 2020 [bibtex] [url]

J. R. Kauffmann, L. Ruff, G. Montavon, K. Müller, The Clever Hans Effect in Anomaly Detection
CoRR, abs/2006.10609, 2020 [bibtex]

A. v. Lühmann, X. Li, N. Gilmore, D. A. Boas, M. A. Yücel, Open Access Multimodal fNIRS Resting State Dataset With and Without Synthetic Hemodynamic Responses
Frontiers in Neuroscience, 14:579353, 2020 [bibtex] [url]

A. v. Lühmann, A. Ortega-Martinez, D. A. Boas, M. A. Yücel, Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective
Frontiers in Human Neuroscience, 14:30, 2020 [bibtex] [url]

A. v. Lühmann, X. Li, K. Müller, D. A. Boas, M. A. Yücel, Improved physiological noise regression in fNIRS: A multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysis
NeuroImage, 208:116472, 2020 [bibtex] [url]

S. Letzgus, Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models
Wind Energy Science, 5(4):1375-1397, 2020 [bibtex] [url]

K. Melnyk, S. Klus, G. Montavon, T. O. Conrad, GraphKKE: graph Kernel Koopman embedding for human microbiome analysis
Appl. Netw. Sci., 5(1):96, 2020 [bibtex]

K. A. Nicoli, S. Nakajima, N. Strodthoff, W. Samek, K. Müller, P. Kessel, Asymptotically Unbiased Estimation of Physical Observables with Neural Samplers
Physical Review E, 101(023304), 2020 [bibtex]

H. E. Sauceda, M. Gastegger, S. Chmiela, K. Müller, A. Tkatchenko, Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields
The Journal of Chemical Physics, 153(12):124109, 2020 [bibtex] [url]

R. A. Vandermeulen, Improving nonparametric density estimation with tensor decompositions
arXiv preprint arXiv:2010.02425, 2020 [bibtex]

Book chapters

G. Montavon, Introduction to Neural Networks
Springer International Publishing, 2020 [bibtex] [url]

P. Seegerer, A. Binder, R. Saitenmacher, M. Bockmayr, M. Alber, P. Jurmeister, F. Klauschen, K. Müller, Interpretable deep neural network to predict estrogen receptor status from haematoxylin-eosin images
Artificial Intelligence and Machine Learning for Digital Pathology, Springer, 2020 [bibtex]

Conference papers

C. J. Anders, P. Pasliev, A. Dombrowski, K. Müller, P. Kessel, Fairwashing explanations with off-manifold detergent
Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, PMLR, Proceedings of Machine Learning Research, 119:314-323, 2020 [bibtex] [url]

C. Cai, A. Hashemi, M. Diwakar, S. Haufe, K. Sekihara, S. S. Nagarajan, Noise Learning in Empirical Bayesian Source Reconstruction Algorithms for Electromagnetic Brain Imaging
The Organization for Human Brain Mapping (OHBM), 2020 [bibtex]

A. Hashemi, C. Cai, K. Müller, S. S. Nagarajan, S. Haufe, Joint Hierarchical Bayesian Learning of Full-structure Noise for Brain Source Imaging
Thirty-Forth Conference on Neural Information Processing Systems (NeurIPS), Medical Imaging meets NeurIPS (Med-NeurIPS) Workshop, 2020 [bibtex] [url]

A. Hashemi, C. Cai, G. Kutyniok, K. Müller, S. Nagarajan, S. Haufe, Electromagnetic Brain Imaging using Sparse Bayesian Learning – Noise Learning and Model Selection
The Organization for Human Brain Mapping (OHBM), 2020 [bibtex]

F. Horn, R. Pack, M. Rieger, The autofeat Python Library for Automatic Feature Engineering and Selection
Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019, Springer International Publishing, 2020 [bibtex] [pdf]

M. Kohlbrenner, A. Bauer, S. Nakajima, A. Binder, W. Samek, S. Lapuschkin, Towards Best Practice in Explaining Neural Network Decisions with LRP
Proceedings of International Joint Conference on Neural Networks (IJCNN2020), 2020 [bibtex]

A. v. Lühmann, B. B. Zimmermann, A. Ortega-Martinez, N. Perkins, M. A. Yücel, D. A. Boas, Towards Neuroscience in the Everyday World: Progress in wearable fNIRS instrumentation and applications
Biophotonics Congress: Biomedical Optics 2020 (Translational, Microscopy, OCT, OTS, BRAIN), Optica Publishing Group, 2020 [bibtex] [url]

W. Mustafa, R. A. Vandermeulen, M. Kloft, Input Hessian Regularization of Neural Networks
International Conference on Machine Learning: Workshop on Beyond First Order Methods in Machine Learning, 2020 [bibtex]

A. Ritchie, R. A. Vandermeulen, C. Scott, Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations
Advances in Neural Information Processing Systems, Curran Associates, Inc., 33:11676-11686, 2020 [bibtex] [url]

S. Schwarzmann, C. C. Marquezan, R. Trivisonno, S. Nakajima, T. Zinner, Accuracy vs. Cost Trade-off for Machine Learning Based QoE Estimation in 5G Networks
Proceedings of IEEE International Conference on Communications (ICC2020), 2020 [bibtex]

V. Srinivasan, K. Müller, W. Samek, S. Nakajima, Benign Examples: Imperceptible changes can enhance image translation performance
Proceedings of Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI2020), 2020 [bibtex]

R. A. Vandermeulen, R. Saitenmacher, A. Ritchie, A Proposal for Supervised Density Estimation
NeurIPS Pre-Registration Workshop, 2020 [bibtex]

2019

Books

S. Nakajima, K. Watanabe, M. Sugiyama, Variational Bayesian Learning Theory
Cambridge University Press, 2019 [bibtex]

W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, K. Müller, (Eds.), Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
Springer, Lecture Notes in Computer Science, 11700, 2019 [bibtex]

Journal papers

M. Alber, S. Lapuschkin, P. Seegerer, M. Hägele, K. T. Schütt, G. Montavon, W. Samek, K. Müller, S. Dähne, P. Kindermans, iNNvestigate neural networks!
Journal of Machine Learning Research, 20(93):1-8, 2019 [bibtex] [url]

S. Bosse, S. Becker, K. Müller, W. Samek, T. Wiegand, Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network
Digital Signal Processing, 91:54-65, 2019 [bibtex] [url]

S. Chmiela, H. Sauceda, I. Poltavsky, K. Müller, A. Tkatchenko, sGDML: Constructing accurate and data efficient molecular force fields using machine learning
Computer Physics Communications, 240:38-45, 2019 [bibtex] [url]

A. Dombrowski, M. Alber, C. J. Anders, M. Ackermann, K. Müller, P. Kessel, Explanations can be manipulated and geometry is to blame
Advances in Neural Information Processing Systems 32, 2019 [bibtex]

M. Hägele, P. Seegerer, S. Lapuschkin, M. Bockmayr, W. Samek, F. Klauschen, K. Müller, A. Binder, Resolving challenges in deep learning-based analyses of histopathological images using explanation methods
CoRR, abs/1908.06943, 2019 [bibtex] [pdf]

L. Helmers, F. Horn, F. Biegler, T. Oppermann, K. Müller, Automating the search for a patent's prior art with a full text similarity search
PLoS ONE, Public Library of Science, 14(3):e0212103, 2019 [bibtex] [pdf] [url]

F. Horst, S. Lapuschkin, W. Samek, K. Müller, W. Schöllhorn, Explaining the unique nature of individual gait patterns with deep learning
Scientific Reports, 9(1):2391, 2019 [bibtex] [url]

P. Jurmeister, M. Bockmayr, P. Seegerer, T. Bockmayr, D. Treue, G. Montavon, C. Vollbrecht, A. Arnold, D. Teichmann, K. Bressem, others, Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases
Science translational medicine, American Association for the Advancement of Science, 11(509), 2019 [bibtex]

P. Jurmeister, M. Bockmayr, P. Seegerer, T. Bockmayr, D. Treue, G. Montavon, C. Vollbrecht, A. Arnold, D. Teichmann, K. Bressem, U. Schüller, M. v. Laffert, K. Müller, D. Capper, F. Klauschen, Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases
Science Translational Medicine, 11(509), 2019 [bibtex] [url]

J. Kauffmann, M. Esders, G. Montavon, W. Samek, K. Müller, From Clustering to Cluster Explanations via Neural Networks
CoRR, abs/1906.07633, 2019 [bibtex] [pdf] [url]

A. v. Lühmann, Z. Boukouvalas, K. Müller, T. Adalı, A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy
NeuroImage, 200:72-88, 2019 [bibtex] [url]

A. v. Lühmann, Z. Boukouvalas, K. Müller, T. Adali, A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy
NeuroImage, 200:72-88, 2019 [bibtex] [url]

S. Lapuschkin, S. Wäldchen, A. Binder, G. Montavon, W. Samek, K. Müller, Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Nature Communications, 10:1096, 2019 [bibtex] [url]

K. Nicoli, P. Kessel, N. Strodthoff, W. Samek, K. Müller, S. Nakajima, Comment on" Solving Statistical Mechanics Using VANs": Introducing saVANt-VANs Enhanced by Importance and MCMC Sampling
arXiv preprint arXiv:1903.11048, 2019 [bibtex]

K. Nicoli, P. Kessel, N. Strodthoff, W. Samek, K. Müller, S. Nakajima, Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling
CoRR, abs/1903.11048, 2019 [bibtex] [pdf] [url]

L. Ruff, R. Vandermeulen, N. Görnitz, A. Binder, E. Müller, K. Müller, M. Kloft, Deep Semi-Supervised Anomaly Detection
CoRR, abs/1906.02694, 2019 [bibtex] [pdf] [url]

F. Sattler, K. Müller, W. Samek, Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints
CoRR, abs/1910.01991, 2019 [bibtex] [pdf] [url]

F. Sattler, S. Wiedemann, K. Müller, W. Samek, Robust and Communication-Efficient Federated Learning from Non-IID Data
CoRR, abs/1903.02891, 2019 [bibtex] [pdf] [url]

H. Sauceda, S. Chmiela, I. Poltavsky, K. Müller, A. Tkatchenko, Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights
CoRR, abs/1909.08565, 2019 [bibtex] [pdf]

H. Sauceda, S. Chmiela, I. Poltavsky, K. Müller, A. Tkatchenko, Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
The Journal of Chemical Physics, 150(11), 2019 [bibtex] [url]

K. Schütt, M. Gastegger, A. Tkatchenko, K. Müller, R. Maurer, Unifying machine learning and quantum chemistry - a deep neural network for molecular wavefunctions
CoRR, abs/1906.10033, 2019 [bibtex] [pdf] [url]

K. Schütt, P. Kessel, M. Gastegger, K. Nicoli, A. Tkatchenko, K. Müller, SchNetPack: A Deep Learning Toolbox For Atomistic Systems
Journal of chemical theory and computation, ACS Publications, 15(1):448-455, 2019 [bibtex]

G. Schwenk, R. Pabst, K. Müller, Classification of structured validation data using stateless and stateful features
Computer Communications, 138:54-66, 2019 [bibtex] [url]

V. Srinivasan, E. Kuruoglu, K. Müller, W. Samek, S. Nakajima, Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution
CoRR, abs/1904.05586, 2019 [bibtex] [pdf] [url]

C. Vidaurre, A. Murguialday, S. Haufe, M. Gómez, K. Müller, V. Nikulin, Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation
NeuroImage, 199:375-386, 2019 [bibtex] [url]

C. Vidaurre, G. Nolte, I. d. Vries, M. Gómez, T. Boonstra, K. Müller, A. Villringer, V. Nikulin, Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets
NeuroImage, 201, 2019 [bibtex] [url]

S. Wiedemann, K. Müller, W. Samek, Compact and Computationally Efficient Representation of Deep Neural Networks
IEEE Transactions on Neural Networks and Learning Systems, 2019 [bibtex] [url]

J. Zhou, I. Tsang, S. Ho, K. Müller, N-ary decomposition for multi-class classification
Machine Learning, 108(5):809-830, 2019 [bibtex] [url]

Book chapters

C. Anders, G. Montavon, W. Samek, K. Müller, Understanding Patch-Based Learning of Video Data by Explaining Predictions
Springer International Publishing, 2019 [bibtex] [url]

L. Arras, J. Arjona-Medina, M. Widrich, G. Montavon, M. Gillhofer, K. Müller, S. Hochreiter, W. Samek, Explaining and Interpreting LSTMs
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Lecture Notes in Computer Science, 11700:211-238, 2019 [bibtex] [url]

G. Montavon, Gradient-Based Vs. Propagation-Based Explanations: An Axiomatic Comparison
Springer International Publishing, 2019 [bibtex] [url]

G. Montavon, A. Binder, S. Lapuschkin, W. Samek, K. Müller, Layer-Wise Relevance Propagation: An Overview
Springer International Publishing, 2019 [bibtex] [url]

W. Samek, K. Müller, Towards Explainable Artificial Intelligence
Springer International Publishing, 2019 [bibtex] [url]

K. Schütt, M. Gastegger, A. Tkatchenko, K. Müller, Quantum-Chemical Insights from Interpretable Atomistic Neural Networks
Springer International Publishing, 2019 [bibtex] [url]

Conference papers

L. Arras, A. Osman, K. Müller, W. Samek, Evaluating Recurrent Neural Network Explanations
Proceedings of the ACL'19 Workshop on BlackboxNLP, Association for Computational Linguistics, 2019 [bibtex]

A. Bauer, S. Nakajima, N. Görnitz, K. Müller, Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs
Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS2019), 38, 2019 [bibtex]

N. Gebauer, M. Gastegger, K. Schütt, Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Advances in Neural Information Processing Systems 32, Curran Associates, Inc., 2019 [bibtex] [url]

A. Hashemi, H. Andrade Loarca, G. Kutyniok, S. Haufe, K. Müller, Deep Brain Source Imaging: An LSTM-inspired Approach for EEG Source Localization based on Sparse Bayesian Learning
Signal Processing with Adaptive Sparse Structured Representations (SPARS), 2019 [bibtex]

S. Iravani, T. Conrad, Deep Learning for Proteomics Data for Feature Selection and Classification
Springer International Publishing, 2019 [bibtex] [url]

K. Müller, Explainable Deep Learning for Analysing Brain Data
2019 7th International Winter Conference on Brain-Computer Interface (BCI), 2019 [bibtex] [url]

S. Redyuk, Automated Documentation of End-to-End Experiments in Data Science
2019 IEEE 35th International Conference on Data Engineering (ICDE), 2019 [bibtex] [url]

L. Ruff, Y. Zemlyanskiy, R. Vandermeulen, T. Schnake, M. Kloft, Self-attentive, multi-context one-class classification for unsupervised anomaly detection on text
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019 [bibtex]

F. Sattler, S. Wiedemann, K. Müller, W. Samek, Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication
International Joint Conference on Neural Networks, IJCNN 2019 Budapest, Hungary, July 14-19, 2019, 2019 [bibtex] [url]

V. Srinivasan, A. Marban, K. Müller, W. Samek, S. Nakajima, Defense Against Adversarial Attacks by Langevin Dynamics
2019 [bibtex] [url]

V. Srinivasan, A. Marban, K. R. Müller, W. Samek, S. Nakajima, Robustifying Models Against Adversarial Attacks by Langevin Dynamics
ICML Workshop on Uncertainty & Robustness in Deep Learning, 2019 [bibtex]

V. Srinivasan, E. Kuruoglu, K. Müller, W. Samek, S. Nakajima, Black-Box Decision based Adversarial Attack with Symmetric Alpha-stable Distribution
Proceedings of the European Signal Processing Conference (EUSIPCO2019), 2019 [bibtex]

P. Wagner, J. Morath, A. Zychlinsky, K. Müller, W. Samek, Rotation Invariant Clustering of 3D Cell Nuclei Shapes*
41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2019 [bibtex] [url]

2018

Journal papers

S. Bosse, D. Maniry, K. Müller, T. Wiegand, W. Samek, Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
IEEE Trans. Image Processing, 27(1):206-219, 2018 [bibtex] [url]

S. Chmiela, H. E. Sauceda, K. Müller, A. Tkatchenko, Towards exact molecular dynamics simulations with machine-learned force fields
Nature Communications, 9(1):3887, 2018 [bibtex] [url]

N. Görnitz, L. A. Lima, K. Müller, M. Kloft, S. Nakajima, Support Vector Data Descriptions and K-means Clustering: One Class?
IEEE Transactions on Neural Networks and Learning Systems, 29(9):3994-4006, 2018 [bibtex]

N. Görnitz, L. A. Lima, L. E. Varella, K. Müller, S. Nakajima, Transductive Regression for Data with Latent Dependency Structure
IEEE Transactions on Neural Networks and Learning Systems, 29(7):2743-2756, 2018 [bibtex]

N. W. Gebauer, M. Gastegger, K. T. Schütt, Generating equilibrium molecules with deep neural networks
CoRR, abs/1810.11347, 2018 [bibtex] [pdf] [url]

D. Heim, G. Montavon, P. Hufnagl, K. Müller, F. Klauschen, Computational analysis reveals histotype-dependent molecular profile and actionable mutation effects across cancers
Genome Medicine, 10(1):83, 2018 [bibtex] [url]

F. Horn, K. Müller, Predicting Pairwise Relations with Neural Similarity Encoders
Bulletin of the Polish Academy of Sciences: Technical Sciences, Polish Academy of Sciences, 66(6):821-830, 2018 [bibtex] [pdf]

S. Kaltenstadler, S. Nakajima, K. Müller, W. Samek, Wasserstein Stationary Subspace Analysis
IEEE Journal of Selected Topics in Signal Processing, 12(6):1213-1223, 2018 [bibtex]

J. Kauffmann, G. Montavon, L. A. Lima, S. Nakajima, K. Müller, N. Görnitz, Unsupervised Detection and Explanation of Latent-class Contextual Anomalies
CoRR, abs/1806.11326, 2018 [bibtex] [url]

G. Montavon, W. Samek, K. Müller, Methods for interpreting and understanding deep neural networks
Digital Signal Processing, 73:1-15, 2018 [bibtex] [url]

W. Pronobis, D. Panknin, J. Kirschnick, V. Srinivasan, W. Samek, V. Markl, M. Kaul, K. Müller, S. Nakajima, Sharing hash codes for multiple purposes
Japanese Journal of Statistics and Data Science, Springer, 1(1):215-246, 2018 [bibtex]

W. Pronobis, D. Panknin, J. Kirschnick, V. Srinivasan, W. Samek, V. Markl, M. Kaul, K. Müller, S. Nakajima, Sharing Hash Codes for Multiple Purposes
Japanese Journal of Statistics and Data Science, 1(1):215-246, 2018 [bibtex]

K. T. Schütt, H. E. Sauceda, P. Kindermans, A. Tkatchenko, K. Müller, SchNet-A deep learning architecture for molecules and materials
The Journal of Chemical Physics, AIP Publishing, 148(24):241722, 2018 [bibtex]

J. Shin, A. v. Lühmann, D. Kim, J. Mehnert, H. Hwang, K. Müller, Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset
Scientific Data, 5(180003), 2018 [bibtex]

S. Wiedemann, A. Marban, K. Müller, W. Samek, Entropy-Constrained Training of Deep Neural Networks
2018 [bibtex] [pdf] [url]

Conference papers

M. Alber, I. Bello, B. Zoph, P. Kindermans, P. Ramachandran, Q. Le, Backprop Evolution
ICML 2018 AutoML Workshop, 2018 [bibtex] [pdf] [url]

A. Flinth, A. Hashemi, Approximate Recovery of Initial Point-like and Instantaneous Sources from Coarsely Sampled Thermal Fields via Infinite-Dimensional Compressed Sensing
2018 26th European Signal Processing Conference (EUSIPCO), 2018 [bibtex] [url]

A. Hashemi, S. Haufe, Improving EEG Source Localization Through Spatio-Temporal Sparse Bayesian Learning
2018 26th European Signal Processing Conference (EUSIPCO), 2018 [bibtex] [url]

P. Kindermans, K. T. Schütt, M. Alber, K. Müller, D. Erhan, B. Kim, S. Dähne, Learning how to explain neural networks: PatternNet and PatternAttribution
6th International Conference on Learning Representations, 2018 [bibtex] [pdf]

F. Klauschen, K. Müller, A. Binder, M. Bockmayr, M. Hägele, P. Seegerer, S. Wienert, G. Pruneri, S. De Maria, S. Badve, others, Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning
Seminars in cancer biology, 52:151-157, 2018 [bibtex]

K. Nicoli, P. Kessel, M. Gastegger, K. Schütt, Analysis of Atomistic Representations Using Weighted Skip-Connections
2018 [bibtex] [pdf] [url]

L. Ruff, R. A. Vandermeulen, N. Görnitz, L. Deecke, S. A. Siddiqui, A. Binder, E. Müller, M. Kloft, Deep One-Class Classification
International Conference on Machine Learning (ICML), 2018 [bibtex] [url]

2017

Journal papers

M. Alber, J. Zimmert, U. Dogan, M. Kloft, Distributed optimization of multi-class SVMs
PLOS ONE, Public Library of Science, 12(6):1-18, 2017 [bibtex] [pdf] [url]

L. Arras, F. Horn, G. Montavon, K. Müller, W. Samek, "What is relevant in a text document?": An interpretable machine learning approach
PLOS ONE, Public Library of Science (PLoS), 12(8):e0181142, 2017 [bibtex] [url]

A. Bauer, M. L. Braun, K. Müller, Accurate Maximum-Margin Training for Parsing With Context-Free Grammars
IEEE Trans. Neural Netw. Learning Syst., 28(1):44-56, 2017 [bibtex] [url]

A. Bauer, S. Nakajima, K. Müller, Efficient Exact Inference with Loss Augmented Objective in Structured Learning
IEEE Transactions on Neural Networks and Learning Systems, 28(11):2566-2579, 2017 [bibtex]

S. Bosse, L. Acqualagna, W. Samek, A. K. Porbadnigk, G. Curio, B. Blankertz, K. Muller, T. Wiegand, Assessing perceived image quality using steady-state visual evoked potentials and spatio-spectral decomposition
IEEE Transactions on Circuits and Systems for Video Technology, IEEE, 2017 [bibtex]

S. Chmiela, A. Tkatchenko, H. E. Sauceda, I. Poltavsky, K. T. Schütt, K. Müller, Machine learning of accurate energy-conserving molecular force fields
Science Advances, American Association for the Advancement of Science, 3(5):e1603015, 2017 [bibtex] [pdf] [url]

A. Flinth, A. Hashemi, Thermal source localization through infinite-dimensional compressed sensing
arXiv preprint arXiv:1710.02016, 2017 [bibtex] [url]

A. v. Lühmann, H. Wabnitz, T. Sander, K. Müller, M3BA: A Mobile, Modular, Multimodal Biosignal Acquisition Architecture for Miniaturized EEG-NIRS-Based Hybrid BCI and Monitoring, Publisher: IEEE
IEEE Transactions on Biomedical Engineering, 64(6):1199-1210, 2017 [bibtex] [url]

A. v. Lühmann, H. Wabnitz, T. Sander, K. Müller, M3BA: A Mobile, Modular, Multimodal Biosignal Acquisition Architecture for Miniaturized EEG-NIRS-Based Hybrid BCI and Monitoring
IEEE Transactions on Biomedical Engineering, IEEE, 64(6):1199-1210, 2017 [bibtex] [url]

L. A. Lima, N. Görnitz, L. E. Varella, M. Vellasco, K. Müller, S. Nakajima, Porosity estimation by semi-supervised learning with sparsely available labeled samples
Computers & Geosciences, 106:33-48, 2017 [bibtex] [url]

L. A. Lima, N. Görnitz, L. E. Varella, M. Vellascob, K. Müller, S. Nakajima, Porosity Estimation by Semi-supervised Learning with Sparsely Available Labeled Samples
Computers and Geosciences, 106:33-48, 2017 [bibtex]

W. Liu, I. W. Tsang, K. Müller, An Easy-to-hard Learning Paradigm for Multiple Classes and Multiple Labels
Journal of Machine Learning Research, 18:94:1-94:38, 2017 [bibtex] [url]

S. Mandt, F. Wenzel, S. Nakajima, J. Cunningham, C. Lippert, M. Kloft, Sparse Probit Linear Mixed Model
Machine Learning (ECMLPKDD 2017 Special Issue), 106:1621-1642, 2017 [bibtex] [url]

G. Montavon, S. Lapuschkin, A. Binder, W. Samek, K. Müller, Explaining nonlinear classification decisions with deep Taylor decomposition
Pattern Recognition, 65:211 - 222, 2017 [bibtex] [url]

W. Samek, T. Wiegand, K. Müller, Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
CoRR, abs/1708.08296, 2017 [bibtex] [url]

W. Samek, A. Binder, G. Montavon, S. Lapuschkin, K. Müller, Evaluating the Visualization of What a Deep Neural Network Has Learned
IEEE Trans. Neural Netw. Learning Syst., 28(11):2660-2673, 2017 [bibtex] [url]

W. Samek, S. Nakajima, M. Kawanabe, K. Müller, On Robust Parameter Estimation in Brain-Computer Interfacing
Journal of Neural Engineering, 14m, 061001, 2017 [bibtex]

K. T. Schütt, F. Arbabzadah, S. Chmiela, K. Müller, A. Tkatchenko, Quantum-chemical insights from deep tensor neural networks
Nature communications, Nature Publishing Group, 8:13890, 2017 [bibtex] [url]

J. Shin, A. v. Lühmann, B. Blankertz, D. Kim, J. Jeong, H. Hwang, K. Müller, Open Access Dataset for EEG+ NIRS Single-Trial Classification, Publisher: IEEE
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(10):1735-1745, 2017 [bibtex]

J. Shin, A. v. Lühmann, B. Blankertz, D. Kim, J. Jeong, H. Hwang, K. Müller, Open Access Dataset for EEG+ NIRS Single-Trial Classification
IEEE Transactions on Neural Systems and Rehabilitation Engineering, IEEE, 25(10):1735-1745, 2017 [bibtex]

Book chapters

A. Flinth, A. Hashemi, G. Kutyniok, Compressed Sensing: From Theory to Praxis
Compressive Sensing of Earth Observations, CRC Press, 2017 [bibtex]

Conference papers

M. Alber, P. Kindermans, K. Schütt, K. Müller, F. Sha, An Empirical Study on The Properties of Random Bases for Kernel Methods
Advances in Neural Information Processing Systems 30 (NIPS 2017), 2017 [bibtex] [pdf] [url]

L. Arras, G. Montavon, K. Müller, W. Samek, Explaining Recurrent Neural Network Predictions in Sentiment Analysis
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA@EMNLP 2017, Copenhagen, Denmark, September 8, 2017, 2017 [bibtex] [url]

S. Brandl, A. v. Lühmann, K. Müller, Towards Brain-Computer Interfaces outside the lab: new measuring devices and machine learning challenges
Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017 [bibtex]

S. Dogadov, A. Masegosa, S. Nakajima, Variational Robust Subspace Clustering with Mean Update Algorithm
ICCV Workshop on Robust Subspace Learning and Applications in Computer Vision (RSL-CV), 2017 [bibtex]

A. Flinth, A. Hashemi, Soft Recovery in Infinite-Dimensional Compressed Sensing with Applications in Thermal Source Localization and Massive MIMO
International Matheon Conference on Compressed Sensing and its Applications (CSA), 2017 [bibtex]

J. Höner, S. Nakajima, A. Bauer, K. Müller, N. Görnitz, Minimizing Trust Leaks for Robust Sybil Detection
Proceedings of 34th International Conference on Machine Learning (ICML2017), 2017 [bibtex]

F. Horn, Context encoders as a simple but powerful extension of word2vec
Proceedings of the 2nd Workshop on Representation Learning for NLP, Association for Computational Linguistics, 2017 [bibtex] [pdf]

J. Y. Koh, W. Samek, K. Müller, A. Binder, Object Boundary Detection and Classification with Image-Level Labels
Pattern Recognition - 39th German Conference, GCPR 2017, Basel, Switzerland, September 12-15, 2017, Proceedings, 2017 [bibtex] [url]

A. v. Lühmann, K. Müller, Why build an integrated EEG-NIRS? About the advantages of hybrid bio-acquisition hardware, ISSN: 1557-170X
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017 [bibtex]

A. v. Lühmann, S. Soekadar, K. Müller, B. Blankertz, Headgear for mobile neurotechnology: looking into alternatives for EEG and NIRS probes
Proceedings of the 7th Graz Brain-Computer Interface Conference 2017, Verlag der Technischen Universität Graz, 2017 [bibtex]

A. v. Lühmann, J. Addesa, S. Chandra, A. Das, M. Hayashibe, A. Dutta, Neural interfacing non-invasive brain stimulation with NIRS-EEG joint imaging for closed-loop control of neuroenergetics in ischemic stroke
Proceedings of the 8th International IEEE EMBS Conference On Neural Engineering (NER), 2017 [bibtex]

A. v. Lühmann, S. Soekadar, K. Müller, B. Blankertz, Headgear for mobile neurotechnology: looking into alternatives for EEG and NIRS probes
Proceedings of the 7th Graz Brain-Computer Interface Conference 2017, Verlag der Technischen Universität Graz, 2017 [bibtex] [url]

A. v. Lühmann, K. Müller, Why build an integrated EEG-NIRS? About the advantages of hybrid bio-acquisition hardware
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017 [bibtex]

W. Samek, A. Binder, S. Lapuschkin, K. Müller, Understanding and Comparing Deep Neural Networks for Age and Gender Classification
2017 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2017, Venice, Italy, October 22-29, 2017, 2017 [bibtex] [url]

K. Schütt, P. Kindermans, H. E. Felix, S. Chmiela, A. Tkatchenko, K. Müller, SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Advances in Neural Information Processing Systems 30 (NIPS 2017), 2017 [bibtex] [pdf] [url]

J. Shin, K. Müller, H. Hwang, Hybrid EEG-NIRS brain-computer interface under eyes-closed condition
2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017, Kuala Lumpur, Malaysia, December 12-15, 2017, 2017 [bibtex] [url]

V. Srinivasan, S. Lapuschkin, C. Hellge, K. Müller, W. Samek, Interpretable human action recognition in compressed domain
2017 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2017, New Orleans, LA, USA, March 5-9, 2017, 2017 [bibtex] [url]

2016

Journal papers

L. Acqualagna, L. Botrel, C. Vidaurre, A. Kübler, B. Blankertz, Large-Scale Assessment of a Fully Automatic Co-Adaptive Motor Imagery-Based Brain Computer Interface, Open Access
PloS one, Public Library of Science, 11(2):1-19, 2016 [bibtex] [url]

B. Blankertz, L. Acqualagna, S. Dähne, S. Haufe, M. Schultze-Kraft, I. Sturm, M. Uscumlic, M. Wenzel, G. Curio, K. Müller, The Berlin Brain-Computer Interface: Progress Beyond Communication and Control, Open Access
Frontiers in neuroscience, 10:530, 2016 [bibtex] [url]

S. Brandl, L. Frolich, J. Höhne, K. Müller, W. Samek, Brain-Computer Interfacing under Distraction: An Evaluation Study
Journal of Neural Engineering, 13(5):056012, 2016 [bibtex]

J. Höhne, D. Bartz, M. N. Hebart, K. Müller, B. Blankertz, Analyzing neuroimaging data with subclasses: a shrinkage approach
NeuroImage, 124, Part A:740-751, 2016 [bibtex] [pdf] [url]

S. Lapuschkin, A. Binder, G. Montavon, K. Müller, W. Samek, The Layer-wise Relevance Propagation Toolbox for Artificial Neural Networks
Journal of Machine Learning Research, 17(114):1-5, 2016 [bibtex]

W. Samek, D. A. Blythe, G. Curio, K. Müller, B. Blankertz, V. V. Nikulin, Multiscale temporal neural dynamics predict performance in a complex sensorimotor task
NeuroImage, Elsevier, 141:291-303, 2016 [bibtex]

C. Sannelli, C. Vidaurre, K. Müller, B. Blankertz, Ensembles of adaptive spatial filters increase BCI performance: an online evaluation
Journal of neural engineering, 13(4):046003, 2016 [bibtex] [pdf] [url]

M. Schultze-Kraft, D. Birman, M. Rusconi, C. Allefeld, K. Görgen, S. Dähne, B. Blankertz, J. Haynes, The point of no return in vetoing self-initiated movements, Open Access
Proceedings of the National Academy of Sciences of the United States of America, 113(4):1080-1085, 2016 [bibtex] [url]

M. Schultze-Kraft, S. Dähne, M. Gugler, G. Curio, B. Blankertz, Unsupervised classification of operator workload from brain signals, Open Access
Journal of neural engineering, 13(3):036008, 2016 [bibtex] [url]

M. S. Treder, A. K. Porbadnigk, F. Shahbazi, K. Müller, B. Blankertz, The LDA beamformer: optimal estimation of ERP source time series using linear discriminant analysis
NeuroImage, 129:279-291, 2016 [bibtex] [url]

M. A. Wenzel, R. Schultze-Kraft, F. C. Meinecke, F. Cardinaux, T. Kemp, K. Müller, G. Curio, B. Blankertz, EEG-based usability assessment of 3D shutter glasses
Journal of neural engineering, 13(1):016003, 2016 [bibtex] [url]

Book chapters

L. Naumann, M. Schultze-Kraft, S. Dähne, B. Blankertz, Prediction of Difficulty Levels in Video Games from Ongoing EEG, open access
Symbiotic Interaction, Springer International Publishing, Lecture Notes in Computer Science, 9961, 2016 [bibtex] [url]

Conference papers

F. Arbabzadah, G. Montavon, K. Müller, W. Samek, Identifying Individual Facial Expressions by Deconstructing a Neural Network
Pattern Recognition: 38th German Conference, GCPR 2016, Hannover, Germany, September 12-15, 2016, Proceedings, Springer International Publishing, 2016 [bibtex] [url]

L. Arras, F. Horn, G. Montavon, K. Müller, W. Samek, Explaining Predictions of Non-Linear Classifiers in NLP
Proceedings of the 1st Workshop on Representation Learning for NLP, Association for Computational Linguistics, 2016 [bibtex]

A. Binder, S. Bach, G. Montavon, K. Müller, W. Samek, Layer-Wise Relevance Propagation for Deep Neural Network Architectures
Information Science and Applications (ICISA) 2016, Springer Singapore, 2016 [bibtex] [url]

A. Binder, G. Montavon, S. Lapuschkin, K. Müller, W. Samek, Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers
Artificial Neural Networks and Machine Learning - ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II, Springer International Publishing, 2016 [bibtex] [url]

S. Brandl, K. Müller, W. Samek, Classification of motor imagery with distractions
6th International BCI Meeting in Asilomar, 2016 [bibtex]

A. Hashemi, M. Rostami, N. Cheung, Efficient environmental temperature monitoring using compressed sensing
2016 Data Compression Conference (DCC), 2016 [bibtex] [url]

A. v. Lühmann, K. Müller, M3BA: New Technology for Mobile Hybrid BCIs
Proceedings of the 6th International Brain-Computer Interface Meeting 2016, 2016 [bibtex]

A. v. Lühmann, H. Wabnitz, T. Sander, K. Müller, Miniaturized CW NIRS for integration and hybridization with mobile EEG / ECG / EMG and Accelerometer
Proceedings of the Society for functional Near Infrared Spectroscopy Biennial Meeting 2016, 2016 [bibtex]

A. v. Lühmann, Hybridization of bio-electrical and bio-optical acquisition technology using open fNIRS components
Proceedings of the DGBMT workshop biosignal processing, DGBMT, 2016 [bibtex]

A. v. Lühmann, K. Müller, M3BA: New Technology for Mobile Hybrid BCIs
Proceedings of the 6th International Brain-Computer Interface Meeting 2016, 2016 [bibtex]

A. v. Lühmann, H. Wabnitz, T. Sander, K. Müller, Miniaturized CW NIRS for integration and hybridization with mobile EEG / ECG / EMG and Accelerometer
Proceedings of the Society for functional Near Infrared Spectroscopy Biennial Meeting 2016, 2016 [bibtex]

S. Lapuschkin, A. Binder, G. Montavon, K. Müller, W. Samek, Analyzing Classifiers: Fisher Vectors and Deep Neural Networks
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 [bibtex]

S. Mandt, F. Wenzel, S. Nakajima, J. P. Cunningham, C. Lippert, M. Kloft, Separating Sparse Signals from Correlated Noise in Binary Classification
Causation: Foundation to Application Workshop (in UAI), 2016 [bibtex]

G. Montavon, K. Müller, M. Cuturi, Wasserstein Training of Restricted Boltzmann Machines
Advances In Neural Information Processing Systems 29, Curran Associates, Inc., 2016 [bibtex]

L. B. Naumann, M. Schultze-Kraft, S. Dähne, B. Blankertz, Prediction of difficulty levels in video games from EEG
6th International BCI Meeting in Asilomar, 2016 [bibtex] [url]

2015

Journal papers

L. Acqualagna, S. Bosse, A. K. Porbadnigk, G. Curio, K. Müller, T. Wiegand, B. Blankertz, EEG-based classification of video quality perception using steady state visual evoked potentials (SSVEPs)
Journal of neural engineering, 12(2):026012, 2015 [bibtex] [url]

S. Bach, A. Binder, G. Montavon, F. Klauschen, K. Müller, W. Samek, On Pixel-wise Explanations for Non-Linear Classifier Decisions by Layer-wise Relevance Propagation
PLOS ONE, 10(7):e0130140, 2015 [bibtex] [url]

J. S. Castano-Candamil, J. Höhne, J. Martinez-Vargas, X. An, G. Castellanos-Dominguez, S. Haufe, Solving the EEG Inverse Problem based on Space-Time-Frequency Structured Sparsity Constraints
NeuroImage, 118:598-612, 2015 [bibtex] [url]

S. Dähne, F. Biessman, W. Samek, S. Haufe, D. Goltz, C. Gundlach, A. Villringer, S. Fazli, K. Müller, Multivariate Machine Learning Methods for Fusing Functional Multimodal Neuroimaging Data
Proceedings of the IEEE, 103(9):1507-1530, 2015 [bibtex] [url]

S. Fazli, S. Dähne, W. Samek, F. Biessmann, K. Müller, Learning from more than one data source: data fusion techniques for sensorimotor rhythm-based Brain-Computer Interfaces
Proceedings of the IEEE, 103(6):891-906, 2015 [bibtex]

J. Hahne, S. Dähne, H. Hwang, K. a. Müller, L. C. Parra, Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control
IEEE Transactions on Neural Systems and Rehabilitation Engineering, IEEE, 23(4):1534-4320, 2015 [bibtex]

H. J. Hwang, V. Y. Ferreria, D. Ulrich, T. Kilic, X. Chatziliadis, B. Blankertz, M. Treder, A Gaze Independent Brain-Computer Interface Based on Visual Stimulation through Closed Eyelids, Open Access
Scientific Reports, 5:15890, 2015 [bibtex] [url]

A. v. Lühmann, C. Herff, D. Heger, T. Schultz, Towards a wireless open source instrument: functional Near-Infrared Spectroscopy in mobile neuroergonomics and BCI applications
fronthumneurosci, 9(617), 2015 [bibtex] [url]

A. v. Lühmann, C. Herff, D. Heger, T. Schultz, Towards a wireless open source instrument: functional Near-Infrared Spectroscopy in mobile neuroergonomics and BCI applications
Frontiers in human neuroscience, 9(617), 2015 [bibtex] [url]

G. Müller-Putz, R. Leeb, M. Tangermann, J. Höhne, A. Kübler, F. Cincotti, D. Mattia, R. Rupp, K. Müller, J. Millan, Towards Noninvasive Hybrid Brain Computer Interfaces: Framework, Practice, Clinical Application, and Beyond
Proceedings of the IEEE, PP(99):1-18, 2015 [bibtex] [url]

K. Nagata, J. Kitazono, S. Nakajima, S. Eifuku, R. Tamura, M. Okada, An Exhaustive Search and Stability of Sparse Estimation for Feature Selection Problem
IPSJ Transactions on Mathematical Modeling and Its Applications, 8:25-32, 2015 [bibtex]

S. Nakajima, R. Tomioka, M. Sugiyama, S. D. Babacan, Condition for Perfect Dimensionality Recovery by Variational Bayesian PCA, In press
Journal of Machine Learning Research, 2015 [bibtex]

L. F. Seoane, S. Gabler, B. Blankertz, Images from the mind: BCI image evolution based on rapid serial visual presentation of polygon primitives
Brain-Computer Interfaces, 2(1):40-56, 2015 [bibtex] [url]

I. Sturm, S. Dähne, B. Blankertz, G. Curio, Multi-Variate EEG Analysis as a Novel Tool to Examine Brain Responses to Naturalistic Music Stimuli, Open Access
PloS one, 10:e0141281, 2015 [bibtex] [url]

I. Sturm, M. Treder, D. Miklody, H. Purwins, S. Dähne, B. Blankertz, G. Curio, Extracting the neural representation of tone onsets for separate voices of ensemble music using multivariate EEG analysis
Psychomusicology: Music, Mind, and Brain, 25:366-379, 2015 [bibtex] [url]

B. Venthur, S. Dähne, J. Höhne, H. Heller, B. Blankertz, Wyrm: A Brain-Computer Interface Toolbox in Python, Open Access
Journal of Neuroinformatics, Springer, 13(4):471-486, 2015 [bibtex] [url]

I. Winkler, S. Haufe, A. Porbadnigk, K. Müller, S. Dähne, Identifying Granger causal relationships between neural power dynamics and variables of interest
NeuroImage, 111:489-504, 2015 [bibtex] [url]

D. Wong, H. Hwang, S. Dähne, K. Müller, S. Lee, Effect of Higher Frequency on the Classification of Steady State Visual Evoked Potentials, accepted
Journal of Neural Engineering, 2015 [bibtex]

Conference papers

S. Brandl, J. Höhne, K. Müller, W. Samek, Bringing BCI into everyday life: Motor imagery in a pseudo realistic environment
Proceedings of the 2015 International IEEE Conference on Neural Engineering (NER), 2015 [bibtex]

L. Frolich, I. Winkler, K. Müller, W. Samek, Investigating effects of different artefact types on Motor Imagery BCI
Conf Proc IEEE Eng Med Biol Soc (EMBC), 2015 [bibtex]

S. T. Hansen, I. Winkler, L. K. Hansen, K. Müller, S. Dähne, Fusing Simultaneous EEG and fMRI Using Functional and Anatomical Information
International Workshop on Pattern Recognition in Neuroimaging, 2015, 2015 [bibtex]

S. Haufe, An extendable simulation framework for benchmarking EEG-based brain connectivity estimation methodologies
Conf Proc IEEE Eng Med Biol Soc, 2015:7562-7565, 2015 [bibtex] [pdf]

S. Haufe, Y. Huang, L. C. Parra, A highly detailed FEM volume conductor model based on the ICBM152 average head template for EEG source imaging and TCS targeting
Conf Proc IEEE Eng Med Biol Soc, 2015:5744-5747, 2015 [bibtex] [pdf]

W. Samek, K. Müller, Tacking noise, artifacts and nonstationarity in BCI with robust divergences
Proceedings of the European Signal Processing Conference (EUSIPCO), 2015 [bibtex]

2014

Journal papers

X. An, J. Höhne, D. Ming, B. Blankertz, Exploring Combinations of Auditory and Visual Stimuli for Gaze-Independent Brain-Computer Interfaces, Open Access
PloS one, Public Library of Science, 9(10):e111070, 2014 [bibtex] [url]

D. A. Blythe, S. Haufe, K. Müller, V. V. Nikulin, The effect of linear mixing in the EEG on Hurst exponent estimation, In press
NeuroImage, 2014 [bibtex] [url]

S. Dähne, F. C. Meinecke, S. Haufe, J. Höhne, M. Tangermann, K. Müller, V. V. Nikulin, SPoC: a novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters
NeuroImage, 86(0):111-122, 2014 [bibtex] [url]

S. Dähne, V. V. Nikulin, D. Ramírez, P. J. Schreier, K. Müller, S. Haufe, Finding brain oscillations with power dependencies in neuroimaging data
NeuroImage, 96:334-348, 2014 [bibtex] [pdf] [url]

S. Dähne, N. Wilbert, L. Wiskott, Slow Feature Analysis of Retinal Waves Leads to V1 Complex Cells
PLoS Computational Biology, Public Library of Science, 10(5):e1003564, 2014 [bibtex] [url]

M. Gaebler, F. Biessmann, J. Lamke, K. Müller, H. Walter, S. Hetzer, Stereoscopic depth increases intersubject correlations of brain networks
NeuroImage, Elsevier, 100:427-434, 2014 [bibtex]

J. Höhne, M. Tangermann, Towards User-Friendly Spelling with an Auditory Brain-Computer Interface: The CharStreamer Paradigm, Open Access
PloS one, Public Library of Science, 9(6):e98322, 2014 [bibtex] [url]

J. Höhne, E. M. Holz, P. Staiger-Sälzer, K. Müller, A. Kübler, M. Tangermann, Motor Imagery for Severely Motor-Impaired Patients: Evidence for Brain-Computer Interfacing as Superior Control Solution, Open Access
PloS one, Public Library of Science, 9(8):e104854, 2014 [bibtex] [url]

C. Habermehl, J. Steinbrink, K. Müller, S. Haufe, Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography
Journal of Biomedical Optics, 19(9):096006, 2014 [bibtex]

S. Haufe, S. Dähne, V. V. Nikulin, Dimensionality reduction for the analysis of brain oscillations
NeuroImage, 101:583-597, 2014 [bibtex] [url]

S. Haufe, J. Kim, I. Kim, A. Sonnleitner, M. Schrauf, G. Curio, B. Blankertz, Electrophysiology-based detection of emergency braking intention in real-world driving
Journal of neural engineering, 11(5):056011, 2014 [bibtex] [url]

S. Haufe, F. Meinecke, K. Görgen, S. Dähne, J. Haynes, B. Blankertz, F. Biessmann, On the interpretation of weight vectors of linear models in multivariate neuroimaging, Neuroimage single best paper of 2014 Award.
NeuroImage, 87:96-110, 2014 [bibtex] [url]

M. Kawanabe, W. Samek, K. Müller, C. Vidaurre, Robust Common Spatial filters with a Maxmin Approach
Neural Computation, 26(2):1-28, 2014 [bibtex] [url]

I. Kim, J. Kim, S. Haufe, S. Lee, Detection of Braking Intention in Diverse Situations during Simulated Driving based on EEG Feature Combination, In press
Journal of neural engineering, 2014 [bibtex]

P. Kindermans, M. Tangermann, K. Müller, B. Schrauwen, Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller
Journal of neural engineering, 11(3):035005, 2014 [bibtex] [url]

R. Lorenz, J. Pascual, B. Blankertz, C. Vidaurre, Towards a holistic assessment of the user experience with hybrid BCIs
Journal of neural engineering, IOP Publishing, 11(3):035007, 2014 [bibtex] [url]

A. Martel, S. Dähne, B. Blankertz, EEG Predictors of Covert Vigilant Attention
Journal of neural engineering, IOP Publishing, 11(3):035009, 2014 [bibtex] [url]

W. Samek, M. Kawanabe, K. Müller, Divergence-based Framework for Common Spatial Patterns Algorithms
Biomedical Engineering, IEEE Reviews in, 7:50-72, 2014 [bibtex] [url]

K. T. Schütt, H. Glawe, F. Brockherde, A. Sanna, K. R. Müller, E. K. Gross, How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
Phys. Rev. B, American Physical Society, 89:205118, 2014 [bibtex] [pdf] [url]

A. Sonnleitner, M. S. Treder, M. Simon, S. Willmann, A. Ewald, A. Buchner, M. Schrauf, Analysis and Single-Trial Classification of EEG Alpha Spindles on rolonged Brake Reaction Times During Auditory Distraction in a Real Road-Driving Study
Accident; analysis and prevention, 62:110-118, 2014 [bibtex]

H. Suk, S. Fazli, J. Mehnert, K. Müller, S. Lee, Predicting BCI subject performance using probabilistic spatio-temporal filters, Open Access
PloS one, Public Library of Science, 9(2):e87056, 2014 [bibtex]

M. S. Treder, H. Purwins, D. Miklody, I. Sturm, B. Blankertz, Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification
Journal of neural engineering, 11:026009, 2014 [bibtex] [url]

I. Winkler, S. Brandl, F. Horn, E. Waldburger, C. Allefeld, M. Tangermann, Robust artifactual independent component classification for BCI practitioners
Journal of Neural Engineering, 11(3):035013, 2014 [bibtex] [url]

Book chapters

A. Binder, W. Samek, K. Müller, M. Kawanabe, Machine Learning for Visual Concept Recognition and Ranking for Images
Towards the Internet of Services: The THESEUS Program, Cognitive Technologies, 2014 [bibtex] [url]

Conference papers

S. Dähne, F. Biessmann, F. C. Meinecke, J. Mehnert, S. Fazli, K. Müller, Multimodal integration of electrophysiological and hemodynamic signals
Brain-Computer Interface (BCI), 2014 International Winter Workshop on, 2014 [bibtex]

S. Dähne, S. Haufe, F. Biessmann, F. Meinecke, D. Ramirez, P. Schreier, V. Nikulin, K. Müller, Finding brain oscillations with power dependencies in neuroimaging data
Annual Meeting of the Organization for Human Brain Mapping (OHBM), 2014, 2014 [bibtex]

S. Dähne, V. V. Nikulin, D. Ramirez, P. J. Schreier, K. Müller, S. Haufe, Optimizing spatial filters for the extraction of envelope-coupled neural oscillations
International Workshop on Pattern Recognition in Neuroimaging, 2014, 2014 [bibtex]

S. Dähne, J. Hahne, P. Pawletta, K. Müller, Boosting simultaneous and proportional myoelectric control by combining source power correlation (SPoC) and linear regression
Bernstein Conference, 2014, 2014 [bibtex]

N. Görnitz, A. K. Porbadnigk, A. Binder, C. Sannelli, M. Braun, K. Müller, M. Kloft, Learning and Evaluation in Presence of Non-iid Label Noise
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014 [bibtex]

J. Höhne, B. Blankertz, K. Müller, D. Bartz, Mean shrinkage improves the classification of ERP signals by exploiting additional label information
Proceedings of the 2014 International Workshop on Pattern Recognition in Neuroimaging, 2014 [bibtex] [pdf]

S. Haufe, F. Meinecke, K. Gorgen, S. Dahne, J. Haynes, B. Blankertz, F. Biessmann, Parameter interpretation, regularization and source localization in multivariate linear models
Pattern Recognition in Neuroimaging, 2014 International Workshop on, 2014 [bibtex]

S. Nakajima, I. Sato, M. Sugiyama, K. Watanabe, H. Kobayashi, Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity than MAP
Advances in Neural Information Processing Systems 27 (NIPS2014), 2014 [bibtex]

W. Samek, M. Kawanabe, Robust Common Spatial Patterns by Minimum Divergence Covariance Estimator
Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, 2014 [bibtex] [url]

W. Samek, K. Müller, Information Geometry meets BCI - Spatial filtering using divergences
Brain-Computer Interface (BCI), 2014 International Winter Workshop on, 2014 [bibtex]

PhD theses

W. Samek, On robust spatial filtering of EEG in nonstationary environments
Technische Universität Berlin, 2014 [bibtex] [pdf] [url]

2013

Journal papers

A. Binder, W. Samek, K. Müller, M. Kawanabe, Enhanced Representation and Multi-Task Learning for Image Annotation
Computer Vision and Image Understanding, 117(5):466 - 478, 2013 [bibtex] [url]

S. Cherla, H. Purwins, M. Marchini, Automatic Phrase Continuation from Guitar and Bass Guitar Melodies
Computer Music Journal, 37(3):68-81, 2013 [bibtex] [pdf]

S. Dähne, F. Biessman, F. C. Meinecke, J. Mehnert, S. Fazli, K. Müller, Integration of Multivariate Data Streams With Bandpower Signals
IEEE Transactions on Multimedia, 15(5):1001-1013, 2013 [bibtex] [url]

A. Ewald, F. Shabhazi, G. Nolte, Identifying causal networks of neuronal sources from EEG/MEG data with the phase slope index: a simulation study
Biomed Tech, 22:1 - 14, 2013 [bibtex]

K. Hansen, G. Montavon, F. Biegler, S. Fazli, M. Rupp, M. Scheffler, O. A. v. Lilienfeld, A. Tkatchenko, K. Müller, Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
Journal of Chemical Theory and Computation, 9(8):3404-3419, 2013 [bibtex]

E. M. Holz, J. Höhne, P. Staiger-Sälzer, M. Tangermann, A. Kübler, Brain-computer interface controlled gaming: Evaluation of usability by severely motor restricted end-users, Special Issue: Brain-computer interfacing
Artificial Intelligence in Medicine, Elsevier, 59(2):111 - 120, 2013 [bibtex]

A. Küber, D. Mattia, R. Rupp, M. Tangermann, Facing the challenge: Bringing brain-computer interfaces to end-users
Artificial Intelligence in Medicine, 59(2):55-60, 2013 [bibtex]

G. Montavon, M. L. Braun, T. Krueger, K. Müller, Analyzing Local Structure in Kernel-based Learning: Explanation, Complexity and Reliability Assessment
Signal Processing Magazine, IEEE, 30(4):62-74, 2013 [bibtex] [url]

G. Montavon, M. Rupp, V. Gobre, A. Vazquez-Mayagoitia, K. Hansen, A. Tkatchenko, K. Müller, O. A. v. Lilienfeld, Machine Learning of Molecular Electronic Properties in Chemical Compound Space, to appear
New Journal of Physics, Focus Issue, Novel Materials Discovery, 2013 [bibtex]

M. Panteli, H. Purwins, A Quantitative Comparison of Chrysanthine Theory and Performance Practice of Scale Tuning, Steps, and Prominence of the Octoechos in Byzantine Chant
Journal of New Music Research, 42(3):205-221, 2013 [bibtex] [pdf]

A. K. Porbadnigk, M. S. Treder, B. Blankertz, J. Antons, R. Schleicher, S. Möller, G. Curio, K. Müller, Single-trial analysis of the neural correlates of speech quality perception
Journal of neural engineering, 10(5):056003, 2013 [bibtex]

W. Samek, F. C. Meinecke, K. Müller, Transferring Subspaces Between Subjects in Brain-Computer Interfacing
IEEE transactions on bio-medical engineering, 60(8):2289-2298, 2013 [bibtex]

M. Schreuder, J. Höhne, B. Blankertz, S. Haufe, T. Dickhaus, M. Tangermann, Optimizing ERP Based BCI - a Systematic Evaluation of Dynamic Stopping Methods
Journal of neural engineering, 10(3):036025, 2013 [bibtex]

M. Schreuder, A. Riccio, M. Risetti, S. Dähne, A. Ramsey, J. Williamson, D. Mattia, M. Tangermann, User-Centered Design in BCI - a Case Study
Artificial Intelligence in Medicine, 59(2):71-80, 2013 [bibtex] [url]

C. Vidaurre, J. Pascual, A. Ramos-Murguialday, R. Lorenz, B. Blankertz, N. Birbaumer, K. Müller, Neuromuscular electrical stimulation induced brain patterns to decode motor imagery
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 124(1):1824-1834, 2013 [bibtex] [url]

Conference papers

D. Bartz, K. Müller, Generalizing Analytic Shrinkage for Arbitrary Covariance Structures
Advances in Neural Information Processing Systems 26, 2013 [bibtex]

E. M. Holz, C. Zickler, A. Riccio, J. Höhne, F. Cincotti, M. Tangermann, S. Halder, D. Mattia, A. Kübler, Evaluation of Four Different BCI Prototypes by Severely Motor-Restricted End-Users
Proceedings of the Fifth International Brain-Computer Interface Meeting 2013, Verlag der Technischen Universität Graz, 2013 [bibtex] [pdf]

F. Horn, S. Dähne, Increasing the spectral signal-to-noise ratio of common spatial patterns
5th International BCI Meeting in Asilomar, 2013 [bibtex]

G. R. Müller-Putz, M. Schreuder, M. Tangermann, R. Leeb, J. d. R. Millán, The hybrid Brain-Computer Interface: a bridge to assistive technology?
Proceedings BMT (Biomedizinische Technik) 2013 - Dreiländertagung der Deutschen, Schweizerischen und Österreichischen Gesellschaft für Biomedizinische Technik, Walter de Gruyter, Biomedical Engineering/Biomedizinische Technik, 58(SI-1), 2013 [bibtex]

G. Montavon, K. Müller, Neural Networks for Computational Chemistry: Pitfalls and Recommendations
MRS Online Proceedings Library, 1523, 2013 [bibtex] [url]

W. Samek, A. Binder, K. Müller, Multiple Kernel Learning for Brain-Computer Interfacing
Conf Proc IEEE Eng Med Biol Soc (EMBC), 2013 [bibtex] [url]

W. Samek, D. Blythe, K. Müller, M. Kawanabe, Robust Spatial Filtering with Beta Divergence
Advances in Neural Information Processing Systems 26, MIT Press, 2013 [bibtex] [pdf] [url]

M. Tangermann, P. Kindermans, M. Schreuder, B. Schrauwen, K. Müller, Zero Training for BCI - Reality for BCI Systems Based on Event-Related Potentials
Proceedings BMT (Biomedizinische Technik) 2013 - Dreiländertagung der Deutschen, Schweizerischen und Österreichischen Gesellschaft für Biomedizinische Technik, Walter de Gruyter, Biomedical Engineering/Biomedizinische Technik, 58(SI-1), 2013 [bibtex] [url]

I. Winkler, E. Waldburger, S. Haufe, M. Tangermann, On Classifying Artifactual Independent Components: Generalization Ability to Different Electrode Setups
Proceedings of the Fifth International Brain-Computer Interface Meeting 2013, Verlag der Technischen Universität Graz, 2013 [bibtex] [pdf]

PhD theses

G. Montavon, On layer-wise representations in deep neural networks
Technische Universität Berlin, 2013 [bibtex]

2012

Books

G. Montavon, G. B. Orr, K. Müller, (Eds.), Neural Networks: Tricks of the Trade, Reloaded
Springer, Lecture Notes in Computer Science (LNCS), 7700, 2012 [bibtex] [url]

Journal papers

F. S. Avarvand, A. Ewald, G. Nolte, Localizing True Brain Interactions from EEG and MEG Data with Subspace Methods and Modified Beamformers
Comp. Math. Methods in Medicine, 2012, 2012 [bibtex]

A. Bhattacharyya, F. Biessmann, J. Veit, R. Kretz, G. Rainer, Functional and laminar dissociations between muscarinic and nicotinic cholinergic neuromodulation in the tree shrew primary visual cortex
European Journal of Neuroscience, 35(8):1270-80, 2012 [bibtex]

F. Biessmann, Y. Murayama, N. K. Logothetis, K. Müller, F. C. Meinecke, Improved decoding of neural activity from fMRI signals using non-separable spatiotemporal deconvolutions
NeuroImage, 61(4):1031-1042, 2012 [bibtex] [pdf]

A. Binder, K. Müller, M. Kawanabe, On Taxonomies for Multi-class Image Categorization
International Journal of Computer Vision, 99(3):281-301, 2012 [bibtex] [url]

A. Binder, S. Nakajima, M. Kloft, C. Müller, W. Samek, U. Brefeld, K. Müller, M. Kawanabe, Insights from Classifying Visual Concepts with Multiple Kernel Learning
PLoS ONE, 7(8), 2012 [bibtex] [url]

D. A. Blythe, P. v. Bünau, F. C. Meinecke, K. Müller, Feature Extraction for Change-Point Detection using Stationary Subspace Analysis
IEEE Transactions on Neural Networks and Learning Systems, 23(4):631-643, 2012 [bibtex] [pdf]

D. Deb, J. Lässig, M. Kloft, A Critical Assessment of the Importance of Seedling Age in the System of Rice Intensification (SRI) in Eastern India, in press
Experimental Agriculture, Cambrigde University Press, 2012 [bibtex]

J. v. Erp, F. Lotte, M. Tangermann, Brain-computer interfaces: beyond medical applications
Computer, Published by the IEEE Computer Society, 2012 [bibtex] [pdf] [url]

A. Ewald, L. Marzetti, F. Zappasodi, F. C. Meinecke, G. Nolte, Estimating true brain connectivity from EEG/MEG data invariant to linear and static transformations in sensor space
NeuroImage, 60(1):476 - 488, 2012 [bibtex] [url]

A. Ewald, S. Aristei, G. Nolte, R. A. Rahman, Brain oscillations and functional connectivity during overt language production
Frontiers in Psychology, 3(166), 2012 [bibtex] [url]

S. Fazli, J. Mehnert, J. Steinbrink, G. Curio, A. Villringer, K. Müller, B. Blankertz, Enhanced performance by a Hybrid NIRS-EEG Brain Computer Interface, Open Access
NeuroImage, 59(1):519-529, 2012 [bibtex] [url]

J. Höhne, K. Krenzlin, S. Dähne, M. Tangermann, Natural Stimuli improve Auditory BCIs with respect to Ergonomics and Performance
Journal of neural engineering, 9(4):045003, 2012 [bibtex] [pdf] [url]

S. Hara, Y. Kawahara, T. Washio, P. v. Bünau, T. Tokunaga, K. Yumoto, Separation of stationary and non-stationary sources with a generalized eigenvalue problem
Neural Networks, 33:7-20, 2012 [bibtex] [pdf]

S. Haufe, V. V. Nikulin, K. Müller, G. Nolte, A critical assessment of connectivity measures for EEG data: a simulation study
NeuroImage, 64:120-133, 2012 [bibtex] [pdf] [url]

F. J. Kiraly, P. v. Bünau, F. C. Meinecke, D. A. Blythe, K. Müller, Algebraic Geometric Comparison of Probability Distributions
Journal of Machine Learning Research, 13:855-903, 2012 [bibtex] [pdf]

M. Kloft, G. Blanchard, On the convergence rate of ℓp-norm multiple kernel learning
Journal of Machine Learning Research, 13:2465-2502, 2012 [bibtex]

T. Krueger, D. Panknin, M. Braun, Fast Cross-Validation via Sequential Testing
arxiv CoRR, abs/1206.2248, 2012 [bibtex] [url]

T. Krueger, K. Rieck, Intelligent Defense against Malicious JavaScript Code
PIK - Praxis der Informationsverarbeitung und Kommunikation, 35(1):54-60, 2012 [bibtex]

C. L. Maeder, C. Sannelli, S. Haufe, B. Blankertz, Prestimulus sensorimotor rhythms influence brain-computer interface classification performance
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 20:653-662, 2012 [bibtex] [url]

G. Onose, C. Grozea, A. Anghelescu, C. Daia, C. Sinescu, A. Ciurea, T. Spircu, A. Mirea, I. Andone, A. Spânu, C. Popescu, S. Mihaescu, S. Fazli, M. Danoczy, F. Popescu, On the feasibility of using motor imagery EEG-based brain-computer interface in chronic tetraplegics for assistive robotic arm control: a clinical test and long-term post-trial follow-up, open access
Spinal Cord, Nature Publishing Group, 2012 [bibtex] [url]

W. Samek, C. Vidaurre, K. Müller, M. Kawanabe, Stationary Common Spatial Patterns for Brain-Computer Interfacing
Journal of Neural Engineering, 9(2):026013, 2012 [bibtex] [url]

S. Schaeff, M. S. Treder, B. Venthur, B. Blankertz, Exploring motion VEPs for gaze-independent communication
Journal of neural engineering, 9(4):045006, 2012 [bibtex] [pdf] [url]

N. M. Schmidt, B. Blankertz, M. S. Treder, Online detection of error-related potentials boosts the performance of mental typewriters
BMC neuroscience, 13:19, 2012 [bibtex] [url]

S. Scholler, S. Bosse, M. S. Treder, B. Blankertz, G. Curio, K. Müller, T. Wiegand, Towards a Direct Measure of Video Quality Perception using EEG
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 21(5):2619-2629, 2012 [bibtex] [pdf] [url]

M. Tangermann, K. Müller, A. Aertsen, N. Birbaumer, C. Braun, C. Brunner, R. Leeb, C. Mehring, K. Miller, G. Müller-Putz, G. Nolte, G. Pfurtscheller, H. Preissl, G. Schalk, A. Schlögl, C. Vidaurre, S. Waldert, B. Blankertz, Review of the BCI Competition IV, Open Access
Frontiers in neuroscience, 6(55), 2012 [bibtex] [url]

M. E. Thurlings, A. M. Brouwer, J. B. Van Erp, B. Blankertz, P. J. Werkhoven, Does bimodal stimulus presentation increase ERP components usable in BCIs?
Journal of neural engineering, 9(4):045005, 2012 [bibtex] [url]

M. E. Thurlings, J. B. v. Erp, A. Brouwer, B. Blankertz, P. Werkhoven, Control-Display Mapping in Brain-Computer Interfaces
Ergonomics, 55(5):564-580, 2012 [bibtex] [pdf] [url]

M. S. Treder, Special section on gaze-independent brain-computer interfaces (Editorial)
Journal of neural engineering, 9(4):040201, 2012 [bibtex]

Book chapters

B. Blankertz, M. Tangermann, K. Müller, BCI applications for the general population
Brain-Computer Interfaces - Principles and Practice, Oxford University Press, 2012 [bibtex]

S. Haufe, V. Nikulin, G. Nolte, Alleviating the Influence of Weak Data Asymmetries on Granger-Causal Analyses, 10.1007/978-3-642-28551-6_4
Latent Variable Analysis and Signal Separation, Springer Berlin / Heidelberg, Lecture Notes in Computer Science, 7191:25-33, 2012 [bibtex] [pdf] [url]

F. J. Király, A. Ziehe, K. Müller, An Algebraic Method for Approximate Rank One Factorization of Rank Deficient Matrices
Latent Variable Analysis and Signal Separation, Springer Berlin / Heidelberg, Lecture Notes in Computer Science, 2012 [bibtex] [url]

M. Kloft, Maschinelles Lernen mit mehreren Kernen
Ausgezeichnete Informatikdissertationen 2011. LNI D-12, GI, 2012 [bibtex]

G. Montavon, K. Müller, Deep Boltzmann Machines and the Centering Trick
Neural Networks: Tricks of the trade, Reloaded, Springer, LNCS, 7700, 2012 [bibtex] [pdf] [url]

M. Quek, J. Höhne, R. Murray-Smith, M. Tangermann, Designing future BCIs: Beyond the bit rate
Towards Practical Brain-Computer Interfaces, Springer, 2012 [bibtex]

Conference papers

F. Biessmann, J. Papaioannou, A. Harth, M. L. Jugel, K. Müller, M. Braun, Quantifying Spatiotemporal Dynamics of Twitter Replies to News Feeds
Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2012 [bibtex] [pdf]

F. Biessmann, J. Papaioannou, M. Braun, A. Harth, Canonical Trends: Detecting Trend Setters in Web Data
Proceedings of the International Conference on Machine Learning, 2012 [bibtex] [pdf]

S. Dähne, F. Meinecke, S. Haufe, J. Höhne, M. Tangermann, V. Nikulin, K. Müller, Multi-variate correlation of power spectral density
Annual Meeting of the Organization for Human Brain Mapping (OHBM), 2012, 2012 [bibtex]

S. Fazli, J. Mehnert, J. Steinbrink, B. Blankertz, Using NIRS as a predictor for EEG-based BCI performance
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2012:4911-4914, 2012 [bibtex] [pdf]

J. Höhne, M. Tangermann, How stimulation speed affects Event-Related Potentials and BCI performance
Conf Proc IEEE Eng Med Biol Soc, 2012:1802-1805, 2012 [bibtex] [pdf]

F. J. Kiraly, P. v. Bünau, J. S. Müller, D. A. Blythe, F. C. Meinecke, K. Müller, Regression for sets of polynomial equations
JMLR Workshop and Conference Proc. Vol. 22, 2012 [bibtex] [pdf]

T. Krueger, H. Gascon, N. Krämer, K. Rieck, Learning Stateful Models for Network Honeypots
Proceedings of the 5th ACM Workshop on Security and Artificial Intelligence (AISEC), 2012 [bibtex] [pdf]

G. Montavon, M. Braun, K. Müller, Deep Boltzmann Machines as Feed-Forward Hierarchies
International Conference on Artificial Intelligence and Statistics (AISTATS), 2012 [bibtex] [pdf]

G. Montavon, K. Hansen, S. Fazli, M. Rupp, F. Biegler, A. Ziehe, A. Tkatchenko, O. A. v. Lilienfeld, K. Müller, Learning Invariant Representations of Molecules for Atomization Energy Prediction
Advances in Neural Information Processing Systems 25, 2012 [bibtex] [url]

J. Pascual, F. Velasco-Álvarez, K. Müller, C. Vidaurre, First Study Towards Linear Control of an Upper-Limb Neuroprosthesis with an EEG-based Brain-Computer Interface
Conf Proc IEEE Eng Med Biol Soc, 2012, 2012 [bibtex] [url]

J. Pascual, R. Lorenz, B. Blankertz, C. Vidaurre, Hybrid EEG-based BCI User Interface for Action Selection
Conf Proc International Conference on NeuroRehabilitation, 2012, 2012 [bibtex]

W. Samek, K. Müller, M. Kawanabe, C. Vidaurre, Brain-Computer Interfacing in Discriminative and Stationary Subspaces
Conf Proc IEEE Eng Med Biol Soc (EMBC), 2012 [bibtex] [url]

C. Sannelli, C. Vidaurre, K. Müller, B. Blankertz, Common Spatial Pattern Patches: online evaluation on naive users
Conf Proc IEEE Eng Med Biol Soc, 2012, 2012 [bibtex] [pdf]

B. Schmitz, R. Wiegand, A. v. Lühmann, S. Schulz, A New Capacitive EMG Sensor for Control of the Active Orthosis Orthojacket
Biomedical Engineering / 765: Telehealth / 766: Assistive Technologies, ACTAPRESS, 2012 [bibtex] [url]

M. Schreuder, M. E. Thurlings, A. Brouwer, J. B. v. Erp, M. Tangermann, Exploring the use of direct feedback in ERP-based BCI
Conf Proc IEEE Eng Med Biol Soc, 2012:6707-6710, 2012 [bibtex]

M. Tangermann, J. Höhne, H. Stecher, M. Schreuder, No Surprise - Fixed Sequence Event-Related Potentials for Brain-Computer Interfaces
Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, 2012 [bibtex] [pdf]

B. Venthur, B. Blankertz, Mushu, a free- and open source BCI signal acquisition, written in Python
Conf Proc IEEE Eng Med Biol Soc, 2012, 2012 [bibtex] [pdf]

C. Widmer, M. Kloft, N. Görnitz, G. Rätsch, Efficient Training of Graph-Regularized Multitask SVMs
Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, 2012 [bibtex]

2011

Journal papers

P. Baldi, K. Müller, G. Schneider, Editorial: Charting Chemical Space: Challenges and Opportunities for Artificial Intelligence and Machine Learning
Molecular Informatics, 30(9):751-751, 2011 [bibtex] [url]

D. Bartz, K. Hatrick, C. Hesse, K. Müller, S. Lemm, Directional Variance Adjustment: a novel covariance estimator for high dimensional portfolio optimization
arXiv, 2011 [bibtex]

F. Biessmann, S. M. Plis, F. C. Meinecke, T. Eichele, K. Müller, Analysis of Multimodal Neuroimaging Data
Biomedical Engineering, IEEE Reviews in, 4:26-58, 2011 [bibtex]

A. Binder, S. Nakajima, M. Kloft, C. Müller, W. Samek, U. Brefeld, K. Müller, M. Kawanabe, Insights from Classifying Visual Concepts with Multiple Kernel Learning
arXiv, 2011 [bibtex] [url]

B. Blankertz, S. Lemm, M. S. Treder, S. Haufe, K. Müller, Single-trial analysis and classification of ERP components - a tutorial
NeuroImage, 56:814-825, 2011 [bibtex] [pdf] [url]

D. Boland, M. Quek, M. Tangermann, J. Williamson, R. Murray-Smith, Using Simulated Input into Brain-Computer Interfaces for User-Centred Design
International Journal of Bioelectromagnetism, 13(2):86-87, 2011 [bibtex] [url]

C. Brunner, M. Billinger, C. Vidaurre, C. Neuper, A comparison of univariate, vector, bilinear autoregressive and band power features for brain-computer interfaces
Medical and Biological Engineering and Computing, 2011 [bibtex]

S. Dähne, K. Müller, a. Michael Tangermann, Slow Feature Analysis as a Potential Preprocessing Tool in BCI
International Journal of Bioelectromagnetism, 13(2):100-101, 2011 [bibtex] [url]

S. Fazli, M. Danóczy, J. Schelldorfer, K. Müller, L1-penalized Linear Mixed-Effects Models for high dimensional data with application to BCI
NeuroImage, 56(4):2100 - 2108, 2011 [bibtex] [pdf] [url]

C. Grozea, C. Voinescu, S. Fazli, Bristle-sensors - Low-cost Flexible Passive Dry EEG Electrodes for Neurofeedback and BCI Applications
Journal of neural engineering, 8:025008, 2011 [bibtex] [pdf]

J. Höhne, M. Schreuder, B. Blankertz, K. Müller, M. Tangermann, Novel Paradigms for Auditory ERP Spellers with Spatial Hearing: Two Online Studies
International Journal of Bioelectromagnetism, 13(2):96-97, 2011 [bibtex] [url]

J. Höhne, M. Schreuder, B. Blankertz, M. Tangermann, A novel 9-class auditory ERP paradigm driving a predictive text entry system, Open Access
Frontiers in neuroscience, 5:99, 2011 [bibtex] [url]

K. Hansen, D. Baehrens, T. Schroeter, M. Rupp, K. Müller, Visual Interpretation of Kernel-Based Prediction Models
Molecular Informatics, 30(9):817-826, 2011 [bibtex] [url]

S. Haufe, R. Tomioka, T. Dickhaus, C. Sannelli, B. Blankertz, G. Nolte, K. Müller, Large-Scale EEG/MEG Source Localization with Spatial Flexibility
NeuroImage, 54:851-859, 2011 [bibtex] [pdf] [url]

S. Haufe, M. S. Treder, M. F. Gugler, M. Sagebaum, G. Curio, B. Blankertz, EEG potentials predict upcoming emergency brakings during simulated driving
Journal of neural engineering, 8:056001, 2011 [bibtex] [url]

S. Kleih, A. Riccio, D. Mattia, M. Schreuder, M. Tangermann, C. Zickler, A. Kübler, Motivation affects performance in a P300-Brain-Computer Interface
International Journal of Bioelectromagnetism, 13(1):46-47, 2011 [bibtex] [url]

M. Kloft, U. Brefeld, S. Sonnenburg, A. Zien, Lp-norm Multiple Kernel Learning
Journal of Machine Learning Research, 12:953-997, 2011 [bibtex]

S. Lemm, B. Blankertz, T. Dickhaus, K. Müller, Introduction to machine learning for brain imaging
NeuroImage, 56:387-399, 2011 [bibtex] [pdf] [url]

K. Müller, B. Blankertz, M. Tangermann, G. Curio, Forschen an einer neuen Schnittstelle zum Gehirn: Das Berliner Brain-Computer-Interface
Nova Acta Leopoldina, 110(377):235-257, 2011 [bibtex] [url]

J. Müller, P. v. Bünau, F. Meinecke, F. Király, K. Müller, The Stationary Subspace Analysis Toolbox
Journal of Machine Learning Research, 12:3065-3069, 2011 [bibtex] [pdf]

G. R. Müller-Putz, C. Breitwieser, F. Cincotti, R. Leeb, M. Schreuder, F. Leotta, M. Tavella, L. Bianchi, A. Kreilinger, A. Ramsay, M. Rohm, M. Sagebaum, L. Tonin, C. Neuper, J. d. R. Millán, Tools for Brain-Computer Interaction: a general concept for a hybrid BCI (hBCI), Open Access
Front Neuroinformatics, 5(0), 2011 [bibtex] [url]

G. R. Müller-Putz, C. Breitwieser, M. Tangermann, M. Schreuder, M. Tavella, R. Leeb, F. Cincotti, F. Leotta, C. Neuper, Tobi hybrid BCI: principle of a new assistive method
International Journal of Bioelectromagnetism, 13(3):144-145, 2011 [bibtex] [url]

G. Montavon, M. Braun, K. Müller, Kernel analysis of deep networks
Journal of Machine Learning Research, 12:2563-2581, 2011 [bibtex] [pdf]

J. Pascual, C. Vidaurre, M. Kawanabe, Investigating EEG non-stationarities with robust PCA and its application to improve BCI performance
International Journal of Bioelectromagnetism, 13:50-51, 2011 [bibtex] [url]

F. Rathke, K. Hansen, U. Brefeld, K. Müller, StructRank: A New Approach for Ligand-Based Virtual Screening
J. Chem. Inf. Model., 51:83-92, 2011 [bibtex]

K. Rieck, Self-Learning Network Intrusion Detection
Information Technology (IT), Oldenbourg, 53(3):152-156, 2011 [bibtex] [pdf]

K. Rieck, Similarity Measures for Sequential Data
WIREs: Data Mining and Knowledge Discovery, Wiley, 1(4):296-304, 2011 [bibtex] [pdf]

K. Rieck, P. Trinius, C. Willems, T. Holz, Automatic Analysis of Malware Behavior using Machine Learning
Journal of Computer Security (JCS), IOPress, 19(4):639-668, 2011 [bibtex] [pdf]

C. Sannelli, C. Vidaurre, K. Müller, B. Blankertz, Common Spatial Pattern Patches - an Optimized Filter Ensemble for Adaptive Brain-Computer Interfaces
Journal of neural engineering, 8(2):025012 (7pp), 2011 [bibtex] [url]

M. Schreuder, A. Riccio, F. Cincotti, M. Risetti, B. Blankertz, M. Tangermann, D. Mattia, Putting AMUSE to work: an end-user study
International Journal of Bioelectromagnetism, 13(3):139-140, 2011 [bibtex] [url]

M. Schreuder, T. Rost, M. Tangermann, Listen, you are writing! Speeding up online spelling with a dynamic auditory BCI, Open Access
Frontiers in neuroscience, 5(112), 2011 [bibtex] [url]

M. Sugiyama, M. Yamada, P. v. Bünau, T. Suzuki, T. Kanamori, M. Kawanabe, Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search
Neural Networks, 24(2):183-198, 2011 [bibtex]

M. Tangermann, M. Schreuder, S. Dähne, J. Höhne, S. Regler, A. Ramsay, M. Quek, J. Williamson, R. Murray-Smith, Optimized Stimulation Events for a Visual ERP BCI
International Journal of Bioelectromagnetism, 13(3):119-120, 2011 [bibtex] [url]

M. S. Treder, A. Bahramisharif, N. M. Schmidt, M. v. Gerven, B. Blankertz, Brain-Computer Interfacing using Modulations of Alpha Activity Induced by Covert Shifts of Attention
Journal of neuroengineering and rehabilitation, 8:24, 2011 [bibtex] [url]

M. S. Treder, N. M. Schmidt, B. Blankertz, Gaze-independent brain-computer interfaces based on covert attention and feature attention, Open Access
Journal of neural engineering, 8(6):066003, 2011 [bibtex] [url]

M. S. Treder, N. M. Schmidt, B. Blankertz, Gaze-independent visual brain-computer interfaces, Open Access
International Journal of Bioelectromagnetism, 13(1):11-12, 2011 [bibtex] [url]

M. S. Treder, G. van der Vloed, P. van der Helm, Interactions between constituent single symmetries in multiple symmetry
Atten Percept Psychophys, 73(5):1487-1502, 2011 [bibtex] [url]

C. Vidaurre, M. Kawanabe, P. v. Bünau, B. Blankertz, K. Müller, Toward Unsupervised Adaptation of LDA for Brain-Computer Interfaces
IEEE transactions on bio-medical engineering, 58(3):587 -597, 2011 [bibtex] [url]

C. Vidaurre, C. Sannelli, K. Müller, B. Blankertz, Machine-Learning Based Co-adaptive Calibration
Neural computation, 23(3):791-816, 2011 [bibtex] [pdf] [url]

C. Vidaurre, C. Sannelli, K. Müller, B. Blankertz, Co-adaptive calibration to improve BCI efficiency
Journal of neural engineering, 8(2):025009 (8pp), 2011 [bibtex] [url]

C. Vidaurre, T. H. Sander, A. Schlögl, BiosSig: The free and open source software library for biomedical signal processing
Computational Intelligence and Neuroscience, 2011, 2011 [bibtex] [url]

I. Winkler, S. Haufe, M. Tangermann, Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals
Behavioral and brain functions : BBF, 7(1):30, 2011 [bibtex] [url]

I. Winkler, M. Tangermann, Artifact-Insensitivity of CSP in Motor Imagery BCI
International Journal of Bioelectromagnetism, 13(2):72-73, 2011 [bibtex] [url]

Book chapters

M. Kawanabe, W. Samek, P. v. Bünau, F. Meinecke, An Information Geometrical View of Stationary Subspace Analysis
Artificial Neural Networks and Machine Learning - ICANN 2011, Springer Berlin / Heidelberg, Lecture Notes in Computer Science, 6792:397-404, 2011 [bibtex] [url]

W. Samek, A. Binder, M. Kawanabe, Multi-task Learning via Non-sparse Multiple Kernel Learning
Computer Analysis of Images and Patterns, Springer Berlin / Heidelberg, Lecture Notes in Computer Science, 6854:335-342, 2011 [bibtex] [url]

W. Wojcikiewicz, C. Vidaurre, M. Kawanabe, Improving Classification Performance of BCIs by Using Stationary Common Spatial Patterns and Unsupervised Bias Adaptation
Hybrid Artificial Intelligent Systems, Springer Berlin / Heidelberg, Lecture Notes in Computer Science, 6679:34-41, 2011 [bibtex] [url]

Conference papers

S. Arndt, J. Antons, R. Schleicher, S. Moller, S. Scholler, G. Curio, A Physiological Approach to Determine Video Quality
2011 IEEE International Symposium on Multimedia (ISM), 2011 [bibtex] [url]

A. Binder, W. Samek, M. Kloft, C. Müller, K. Müller, M. Kawanabe, The Joint Submission of the TU Berlin and Fraunhofer FIRST (TUBFI) to the ImageCLEF2011 Photo Annotation Task
CLEF (Notebook Papers/Labs/Workshop), 2011 [bibtex] [pdf]

A. Binder, W. Wojcikiewicz, C. Müller, M. Kawanabe, A Hybrid Supervised-Unsupervised Vocabulary Generation Algorithm for Visual Concept Recognition
Computer Vision - ACCV 2010, Springer Berlin / Heidelberg, Lecture Notes in Computer Science, 6494:95-108, 2011 [bibtex] [url]

D. Blythe, W. Samek, K. Müller, Stationary Linear Discriminant Analysis - Classifying Non-Stationary Features in Brain-Computer Interfacing, Conference Abstract: Bernstein Conference on Computational Neuroscience (BCCN '11)
Frontiers in Neuroscience, 2011 [bibtex] [url]

W. Cho, C. Vidaurre, U. Hoffman, N. Birbaumer, A. Ramos-Murguialday, Afferent and efferent activity control in the design of brain computer interfaces for motor rehabilitation
Conf Proc IEEE Eng Med Biol Soc, 2011:7310-7315, 2011 [bibtex] [url]

S. Dähne, J. Höhne, M. Schreuder, M. Tangermann, Slow Feature Analysis - A Tool for Extraction of Discriminating Event-Related Potentials in Brain-Computer Interfaces
Artificial Neural Networks and Machine Learning - ICANN 2011, Springer Berlin / Heidelberg, Lecture Notes in Computer Science, 6791:36-43, 2011 [bibtex] [url]

S. Dähne, J. Höhne, M. Schreuder, M. Tangermann, Band Power Features Correlate With Performance In Auditory Brain-Computer Interface
Front. Hum. Neurosci. Conference Abstract: XI International Conference on Cognitive Neuroscience (ICON XI), 2011 [bibtex] [url]

S. Dähne, J. Höhne, M. Tangermann, Adaptive Classification Improves Control Performance In ERP-Based BCIs
Proceedings of the 5th International BCI Conference, 2011 [bibtex] [url]

S. Fazli, M. Danóczy, J. Schelldorfer, K. Müller, 1-Penalized Linear Mixed-Effects Models for BCI
Artificial Neural Networks and Machine Learning-ICANN 2011, Springer-Verlag, 2011 [bibtex]

J. Höhne, M. Tangermann, Natural stimuli for auditory BCI
Neurosc Let, 500(Supplement 1):e11, 2011 [bibtex] [pdf] [url]

J. Höhne, M. Tangermann, Stimulation Speed Boosts Auditory BCI Performance
Proc. 5th Int. BCI Conf. Graz, Verlag der Technischen Universität Graz, 2011 [bibtex] [url]

R. Jenssen, M. Kloft, A. Zien, S. Sonnenburg, K. Müller, A New Scatter-Based Multi-Class Support Vector Machine
Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2011 [bibtex]

M. Kawanabe, A. Binder, C. Müller, W. Wojcikiewicz, Multi-modal visual concept classification of images via Markov random walk over tags
Applications of Computer Vision (WACV), 2011 IEEE Workshop on, 2011 [bibtex] [url]

M. Kloft, G. Blanchard, The Local Rademacher complexity of ℓp-norm multiple kernel learning
Advances in Neural Information Processing Systems 24, MIT Press, 2011 [bibtex]

T. Krueger, D. Panknin, M. Braun, Fast Cross-Validation via Sequential Analysis
Neural Information Processing Systems (NIPS), Big Learning Workshop, 2011 [bibtex] [url]

G. Montavon, M. Braun, K. Müller, Importance of Cross-Layer Cooperation for Learning Deep Feature Hierarchies
NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011 [bibtex] [pdf]

J. Pascual, M. Kawanabe, C. Vidaurre, Modelling non-stationarities in EEG data with Robust Principal Component Analysis
Hybrid Artificial Intelligent Systems, Springer, Volume 6679/2011:51-58, 2011 [bibtex] [url]

J. Pascual, A. Ramos, C. Vidaurre, Classifying motor imagery with FES induced EEG patterns
Neuroscience letters, 500(Supplement 1):e48, 2011 [bibtex] [url]

A. K. Porbadnigk, J. Antons, M. S. Treder, B. Blankertz, R. Schleicher, S. Moeller, G. Curio, ERP assessment of word processing under broadcast bit rate limitations
Neuroscience letters, 500, suppl. 1:e49, 2011 [bibtex] [pdf] [url]

A. K. Porbadnigk, S. Scholler, B. Blankertz, A. Ritz, M. Born, R. Scholl, K. Müller, G. Curio, M. S. Treder, Revealing the Neural Response to Imperceptible Peripheral Flicker with Machine Learning
Conf Proc IEEE Eng Med Biol Soc, 2011:3692-3695, 2011 [bibtex] [pdf]

M. Quek, D. Boland, J. Williamson, R. Murray-Smith, M. Tavella, S. Perdikis, M. Schreuder, M. Tangermann, Simulating the feel of brain-computer interfaces for design, development and social interaction
Proceedings of the 2011 annual conference on Human factors in computing systems, ACM, CHI '11, 2011 [bibtex] [url]

U. Rückert, M. Kloft, Transfer Learning with Adaptive Regularizers
Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2011), Springer, 2011 [bibtex]

K. Rieck, Computer Security and Machine Learning: Worst Enemies or Best Friends?
DIMVA Workshop on Systems Security (SYSSEC), 2011 [bibtex] [pdf]

W. Samek, M. Kawanabe, C. Vidaurre, Group-wise Stationary Subspace Analysis - A novel method for studying non-stationarities
Proc. 5th Int. BCI Conf. Graz, Verlag der Technischen Universität Graz, 2011 [bibtex]

S. Schaeff, M. S. Treder, B. Venthur, B. Blankertz, Motion-based ERP spellers in a covert attention paradigm
Neuroscience letters, 500(Supplement 1):e11, 2011 [bibtex] [pdf] [url]

N. Schmidt, B. Blankertz, M. S. Treder, Online detection of error potentials increases information throughput in a brain-computer interface
Neuroscience letters, 500(Supplement 1):e19 - e20, 2011 [bibtex] [url]

M. Schreuder, J. Höhne, M. S. Treder, B. Blankertz, M. Tangermann, Performance Optimization of ERP-Based BCIs Using Dynamic Stopping
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2011 [bibtex] [pdf]

G. Schwenk, K. Rieck, Adaptive Detection of Covert Communication in HTTP Requests
European Conference on Computer Network Defense (EC2ND), 2011 [bibtex]

F. Shahbazi Avarvand, A. Ziehe, G. Nolte, Music algorithm to localize sources with unknown directivity in acoustic imaging
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011 [bibtex] [url]

M. Tangermann, J. Höhne, User-centered brain-computer interface design by optimizing auditory and visual stimuli
Frontiers in Human Neuroscience, 2011 [bibtex] [url]

M. Tangermann, J. Höhne, M. Schreuder, M. Sagebaum, B. Blankertz, A. Ramsay, R. Murray-Smith, Data Driven Neuroergonomic Optimization of BCI Stimuli
Proc. 5th Int. BCI Conf. Graz, 2011 [bibtex]

W. Wojcikiewicz, C. Vidaurre, M. Kawanabe, Stationary Common Spatial Patterns: Towards robust classification of non-stationary EEG signals
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on, 2011 [bibtex] [url]

H. J. Wouters, M. A. v. Gerven, M. S. Treder, T. Heskes, A. Bahramisharif, Covert Attention as a Paradigm for Subject-Independent Brain-Computer Interfacing
MLINI'11, 2011 [bibtex]

F. Yamaguchi, F. Lindner, K. Rieck, Vulnerability Extrapolation: Assisted Discovery of Vulnerabilities using Machine Learning
Proc. of 5th USENIX Workshop on Offensive Technologies (WOOT), 2011 [bibtex] [pdf]

PhD theses

F. Biessmann, Data-driven analysis for multimodal neuroimaging
Berlin Institute of Technology, 2011 [bibtex] [url]

S. Fazli, Advances in Neurotechnology for Brain Computer Interfaces
Technische Universität Berlin, Fakultät IV - Elektrotechnik und Informatik, 2011 [bibtex] [pdf]

S. Haufe, Towards EEG source connectivity analysis
Berlin Institute of Technology, 2011 [bibtex] [pdf] [url]

M. Kloft, p-Norm Multiple Kernel Learning
Berlin Institute of Technology, 2011 [bibtex]

2010

Journal papers

J. Araújo, P. v. Bünau, J. Mitchell, M. Neunhöffer, Computing automorphisms of semigroups
Journal of Symbolic Computation, 45(3):373 - 392, 2010 [bibtex] [url]

D. Baehrens, T. Schroeter, S. Harmeling, M. Kawanabe, K. Hansen, K. Müller, How to Explain Individual Classification Decisions
JMLR, 11:1803-1831, 2010 [bibtex] [url]

B. Blankertz, C. Sannelli, S. Halder, E. M. Hammer, A. Kübler, K. Müller, G. Curio, T. Dickhaus, Neurophysiological Predictor of SMR-Based BCI Performance
NeuroImage, 51(4):1303-1309, 2010 [bibtex] [pdf] [url]

B. Blankertz, M. Tangermann, C. Vidaurre, S. Fazli, C. Sannelli, S. Haufe, C. Maeder, L. E. Ramsey, I. Sturm, G. Curio, K. Müller, The Berlin Brain-Computer Interface: Non-Medical Uses of BCI Technology, Open Access
Frontiers in neuroscience, 4:198, 2010 [bibtex] [url]

S. Haufe, G. Nolte, K. Müller, N. Krämer, Sparse Causal Discovery in Multivariate Time Series
JMLR W&CP, 6:97-106, 2010 [bibtex] [pdf]

S. Haufe, R. Tomioka, G. Nolte, K. Müller, M. Kawanabe, Modeling sparse connectivity between underlying brain sources for EEG/MEG
IEEE transactions on bio-medical engineering, 57(8):1954 - 1963, 2010 [bibtex] [pdf] [url]

T. Lang, M. Toussaint, Planning with Noisy Probabilistic Relational Rules
Journal of Artificial Intelligence Research, 39:1-49, 2010 [bibtex] [pdf]

Y. Murayama, F. Biessmann, F. C. Meinecke, K. Müller, M. Augath, A. Öltermann, N. K. Logothetis, Relationship between neural and hemodynamic signals during spontaneous activity studied with temporal kernel CCA
Magnetic Resonance Imaging, 28(8):1095-1103, 2010 [bibtex] [url]

V. V. Nikulin, K. Linkenkaer-Hansen, G. Nolte, G. Curio, Non-zero mean and asymmetry of neuronal oscillations have different implications for evoked responses.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 121(2):186-93, 2010 [bibtex] [url]

G. Nolte, K. Müller, Localizing and estimating causal relations of interacting brain rhythms, Open Access
Frontiers in human neuroscience, 4:209, 2010 [bibtex] [url]

J. d. R. Millán, R. Rupp, G. Müller-Putz, R. Murray-Smith, C. Giugliemma, M. Tangermann, C. Vidaurre, F. Cincotti, A. Kübler, R. Leeb, C. Neuper, K. Müller, D. Mattia, Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges, Open Access
Frontiers in Neuroprosthetics, 4, 2010 [bibtex] [url]

K. Rieck, T. Krueger, U. Brefeld, K. Müller, Approximate Tree Kernels
Journal of Machine Learning Research, 11(Feb):555-580, 2010 [bibtex] [pdf]

M. Rupp, T. Schroeter, R. Steri, H. Zettl, E. Proschak, K. Hansen, O. Rau, O. Schwarz, L. Müller-Kuhrt, M. Schubert-Zsilavecz, K. Müller, G. Schneider, From machine learning to natural product derivatives selectively activating transcription factor PPARγ
ChemMedChem, 5(2):191-194, 2010 [bibtex] [url]

C. Sannelli, T. Dickhaus, S. Halder, E. M. Hammer, K. Müller, B. Blankertz, On optimal channel configurations for SMR-based brain-computer interfaces
Brain topography, 23(2):186-193, 2010 [bibtex] [pdf] [url]

M. Schreuder, B. Blankertz, M. Tangermann, A New Auditory Multi-class Brain-Computer Interface Paradigm: Spatial Hearing as an Informative Cue, Open Access
PloS one, 5(4):e9813, 2010 [bibtex] [url]

R. Steri, M. Rupp, E. Proschak, T. Schroeter, H. Zettl, K. Hansen, O. Schwarz, L. Müller-Kuhrt, K. Müller, G. Schneider, M. Schubert-Zsilavecz, Truxillic acid derivatives act as peroxisome proliferator-activated receptor [gamma] activators
Bioorganic & Medicinal Chemistry Letters, 20(9):2920-2923, 2010 [bibtex] [url]

I. Sushko, S. Novotarskyi, R. Körner, A. K. Pandey, A. Cherkasov, J. Li, P. Gramatica, K. Hansen, T. Schroeter, K. Müller, L. Xi, H. Liu, X. Yao, TomasÖberg, F. Hormozdiari, P. Dao, C. Sahinalp, R. Todeschini, P. Polishchuk, A. Artemenko, V. Kuz'min, T. M. Martin, D. M. Young, D. Fourches, E. Muratov, A. Tropsha, I. Baskin, D. Horvath, G. Marcou, C. Muller, A. Varnek, V. V. Prokopenko, I. V. Tetko, Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Set
J. Chem. Inf. Model., 50(12):2094-2111, 2010 [bibtex] [url]

R. Tomioka, K. Müller, A regularized discriminative framework for EEG analysis with application to brain-computer interface
NeuroImage, 49:415-432, 2010 [bibtex] [url]

M. S. Treder, Behind the Looking-Glass: A Review on Human Symmetry Perception
Symmetry, 2(3):1510-1543, 2010 [bibtex] [url]

M. S. Treder, B. Blankertz, (C)overt attention and visual speller design in an ERP-based brain-computer interface, Open Access
Behavioral and brain functions : BBF, 6:28, 2010 [bibtex] [url]

B. Venthur, S. Scholler, J. Williamson, S. Dähne, M. S. Treder, M. T. Kramarek, K. Müller, B. Blankertz, Pyff - A Pythonic Framework for Feedback Applications and Stimulus Presentation in Neuroscience, Open Access
Frontiers in neuroscience, 4:179, 2010 [bibtex] [url]

C. Vidaurre, B. Blankertz, Towards a Cure for BCI Illiteracy, Open Access
Brain topography, 23:194-198, 2010 [bibtex] [url]

P. Zwolinski, M. Roszkowski, J. Zygierewicz, S. Haufe, G. Nolte, P. J. Durka, Open database of epileptic EEG with MRI and postoperational assessment of foci - a real world verification for the EEG inverse solutions
Neuroinformatics, 8:285-299, 2010 [bibtex] [pdf] [url]

Book chapters

B. Blankertz, M. Tangermann, C. Vidaurre, T. Dickhaus, C. Sannelli, F. Popescu, S. Fazli, M. Danóczy, G. Curio, K. Müller, Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface
Brain-Computer Interfaces (Revolutionizing Human-Computer Interaction), Springer, The Frontiers Collection, 2010 [bibtex] [url]

A. Ewald, A. Ziehe, F. Shahbazi, G. Nolte, Exploiting Prior Neurophysiological Knowledge to Improve Brain Computer Interface Performance
17th International Conference on Biomagnetism Advances in Biomagnetism - Biomag2010, Springer Berlin Heidelberg, IFMBE Proceedings, 28:370-373, 2010 [bibtex] [url]

G. Nolte, A. Ziehe, N. Krämer, F. Popescu, K. Müller, Comparison of Granger Causality and Phase Slope Index
Causality: Objectives and Assessment, JMLR Workshop and Conference Proceedings, 6:267-276, 2010 [bibtex] [url]

A. Schlögl, C. Vidaurre, K. Müller, Adaptive Methods in BCI Research - An Introductory Tutorial
Brain-Computer Interfaces, Springer, The Frontiers Collection, 2010 [bibtex] [url]

F. Shahbazi, A. Ewald, A. Ziehe, G. Nolte, Constructing Surrogate Data to Control for Artifacts of Volume Conduction for Functional Connectivity Measures
17th International Conference on Biomagnetism Advances in Biomagnetism - Biomag2010, Springer Berlin Heidelberg, IFMBE Proceedings, 28:207-210, 2010 [bibtex] [url]

Conference papers

L. Acqualagna, M. S. Treder, M. Schreuder, B. Blankertz, A novel brain-computer interface based on the rapid serial visual presentation paradigm
Conf Proc IEEE Eng Med Biol Soc, 2010:2686-2689, 2010 [bibtex] [pdf] [url]

P. v. Bünau, F. C. Meinecke, S. Scholler, K. Müller, Finding Stationary Brain Sources in EEG Data
Proceedings of the 32nd Annual Conference of the IEEE EMBS, 2010 [bibtex]

F. Biessmann, A. Harth, Analysing Dependency Dynamics in Web Data
AAAI Spring Symposium: Linked Data meets AI, 2010 [bibtex] [pdf] [url]

A. Binder, M. Kawanabe, Enhancing Recognition of Visual Concepts with Primitive Color Histograms via Non-sparse Multiple Kernel Learning, 10.1007/978-3-642-15751-6_33
Multilingual Information Access Evaluation II. Multimedia Experiments, Springer Berlin / Heidelberg, Lecture Notes in Computer Science, 6242:269-276, 2010 [bibtex] [url]

J. Höhne, M. Schreuder, B. Blankertz, K. Muüller, M. Tangermann, Novel paradigms for auditory P300 Spellers with spatial hearing: two online studies, Conference Abstract: Bernstein Conference on Computational Neuroscience 2010
Frontiers in computational neuroscience, 2010 [bibtex] [url]

J. Höhne, M. Schreuder, B. Blankertz, M. Tangermann, Two-dimensional auditory P300 Speller with predictive text system
Conf Proc IEEE Eng Med Biol Soc, 2010:4185-4188, 2010 [bibtex] [pdf] [url]

S. Hara, Y. Kawahara, T. Washio, P. v. Bünau, Stationary subspace analysis as a generalized eigenvalue problem
Proceedings of the 17th international conference on Neural information processing (ICONIP), Springer-Verlag, 2010 [bibtex]

S. Haufe, R. Tomioka, T. Dickhaus, C. Sannelli, B. Blankertz, G. Nolte, K. Müller, Localization of class-related mu-rhythm desynchronization in motor imagery based Brain-Computer Interface sessions
Conf Proc IEEE Eng Med Biol Soc, 2010:5137-5140, 2010 [bibtex] [pdf] [url]

S. Haufe, R. Tomioka, G. Nolte, K. Müller, M. Kawanabe, Modeling the Connectivity of Neural Ensembles Underlying EEG/MEG, Conference Abstract: Bernstein Conference on Computational Neuroscience 2010
Frontiers in computational neuroscience, 2010 [bibtex] [url]

S. Haufe, M. S. Treder, M. F. Gugler, M. Sagebaum, A. Ewald, G. Curio, B. Blankertz, Neural Signatures Enhance Emergency Braking Intention Detection during Simulated Driving, Conference Abstract: Bernstein Conference on Computational Neuroscience 2010
Frontiers in computational neuroscience, 2010 [bibtex] [url]

M. Kawanabe, J. Pascual, C. Vidaurre, Investigation of Non-stationarity in Brain Activity via Robust Principal Component Analysis
Bernstein Conference on Computational Neuroscience, 2010 [bibtex] [url]

M. Kloft, P. Laskov, Online Anomaly Detection under Adversarial Impact
JMLR Workshop and Conference Proceedings, Volume 9: AISTATS, MIT Press, 2010 [bibtex] [pdf]

M. Kloft, U. Rückert, P. L. Bartlett, A Unifying View of Multiple Kernel Learning
Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 2010 [bibtex] [pdf]

T. Krueger, C. Gehl, K. Rieck, P. Laskov, TokDoc: A Self-Healing Web Application Firewall
Proc. of 25th ACM Symposium on Applied Computing (SAC), 2010 [bibtex] [pdf]

T. Krueger, N. Krämer, K. Rieck, ASAP: Automatic Semantics-Aware Analysis of Network Payloads
Proc. of ECML Workshop on Privacy and Security Issues in Data Mining and Machine Learning (PSDML), 2010 [bibtex] [pdf]

T. Lang, M. Toussaint, K. Kersting, Exploration in Relational Worlds
Proc. of the European Conf. on Machine Learning (ECML), 2010 [bibtex] [pdf]

T. Lang, M. Toussaint, Probabilistic Backward and Forward Reasoning in Stochastic Relational Worlds
Proc. of the Int. Conf. on Machine Learning (ICML), 2010 [bibtex] [pdf]

G. Montavon, M. Braun, K. Müller, Layer-wise analysis of deep networks with Gaussian kernels
Advances in Neural Information Processing Systems 23, 2010 [bibtex] [pdf]

A. K. Porbadnigk, J. Antons, B. Blankertz, M. S. Treder, R. Schleicher, S. Möller, G. Curio, Using ERPs for Assessing the (Sub)Conscious Perception of Noise
Conf Proc IEEE Eng Med Biol Soc, 2010:2690-2693, 2010 [bibtex] [pdf] [url]

A. Porbadnigk, J. Antons, B. Blankertz, M. S. Treder, R. Schleicher, S. Möller, G. Curio, Assessing the (Sub)Conscious Processing of Noise based on ERPs, Conference Abstract: Bernstein Conference on Computational Neuroscience 2010
Frontiers in computational neuroscience, 2010 [bibtex] [url]

K. Rieck, T. Krueger, A. Dewald, Cujo: Efficient Detection and Prevention of Drive-by-Download Attacks
Proc. of 26th Annual Computer Security Applications Conference (ACSAC), 2010 [bibtex] [pdf]

K. Rieck, G. Schwenk, T. Limmer, T. Holz, P. Laskov, Botzilla: Detecting the "Phoning Home" of Malicious Software
Proc. of 25th ACM Symposium on Applied Computing (SAC), 2010 [bibtex] [pdf]

C. Sannelli, C. Vidaurre, K. Müller, B. Blankertz, Common Spatial Pattern Patches - an Optimized Filter Ensemble for Adaptive Brain-Computer Interfaces
Conf Proc IEEE Eng Med Biol Soc, 2010:4351-4354, 2010 [bibtex] [pdf] [url]

C. Sannelli, C. Vidaurre, K. Müller, B. Blankertz, Common Spatial Pattern Patches - an Optimized Spatial Filter for Adaptive BCIs, Conference Abstract: Bernstein Conference on Computational Neuroscience 2010
Frontiers in computational neuroscience, 2010 [bibtex] [url]

N. M. Schmidt, B. Blankertz, M. S. Treder, Alpha-modulation induced by covert attention shifts as a new input modality for EEG-based BCIs
Proceedings of the 2010 IEEE Conference on Systems, Man and Cybernetics (SMC2010), 2010 [bibtex] [pdf] [url]

N. M. Schmidt, B. Blankertz, M. S. Treder, Alpha-modulation induced by covert attention shifts as a new input modality for EEG-based BCIs, Conference Abstract: Bernstein Conference on Computational Neuroscience 2010
Frontiers in computational neuroscience, 2010 [bibtex] [url]

M. Sugiyama, S. Hara, P. v. Bünau, T. Suzuki, T. Kanamori, M. Kawanabe, Direct Density Ratio Estimation with Dimensionality Reduction
Proceedings of 2010 SIAM International Conference on Data Mining (SDM2010), 2010 [bibtex]

M. Toussaint, N. Plath, T. Lang, N. Jetchev, Integrated motor control, planning, grasping and high-level reasoning in a blocksworld using probabilistic inference
IEEE International Conference on Robotics and Automation (ICRA), 2010 [bibtex] [pdf]

M. S. Treder, N. M. Schmidt, B. Blankertz, Towards gaze-independent visual brain-computer interfaces, Conference Abstract: Bernstein Conference on Computational Neuroscience 2010
Frontiers in computational neuroscience, 2010 [bibtex] [url]

P. Trinius, C. Willems, T. Holz, K. Rieck, A Malware Instruction Set for Behavior-based Analysis.
Proc. of Conference "Sicherheit, Schutz und Zuverlässigkeit" (SICHERHEIT), 2010 [bibtex] [pdf]

B. Venthur, B. Blankertz, M. F. Gugler, G. Curio, Novel Applications of BCI Technology: Psychophysiological Optimization of Working Conditions in Industry
Proceedings of the 2010 IEEE Conference on Systems, Man and Cybernetics (SMC2010), 2010 [bibtex] [url]

W. Wojcikiewicz, C. Vidaurre, M. Kawanabe, Stationary Common Spatial Patterns for non-stationary EEG data, Conference Abstract: Bernstein Conference on Computational Neuroscience (BCCN '10)
Frontiers in Neuroscience, 2010 [bibtex] [url]

W. Wojcikiewicz, A. Binder, M. Kawanabe, Enhancing Image Classification with Class-wise Clustered Vocabularies
Pattern Recognition (ICPR), 2010 20th International Conference on, 2010 [bibtex] [url]

W. Wojcikiewicz, A. Binder, M. Kawanabe, Shrinking large visual vocabularies using multi-label agglomerative information bottleneck
Image Processing (ICIP), 2010 17th IEEE International Conference on, 2010 [bibtex] [url]

Technical reports

M. Kloft, U. Brefeld, S. Sonnenburg, A. Zien, Non-Sparse Regularization and Efficient Training with Multiple Kernels
EECS Department, University of California, Berkeley, 2010 [bibtex] [pdf] [url]

M. Kloft, P. Laskov, Security Analysis of Online Centroid Anomaly Detection, CoRR abs/1003.0078
EECS Department, University of California, Berkeley, 2010 [bibtex] [pdf] [url]

I. Schuster, T. Krueger, C. Gehl, K. Rieck, P. Laskov, FIPS: FIRST Intrusion Prevention System
Fraunhofer Institute FIRST, 2010 [bibtex]

2009

Journal papers

P. v. Bünau, F. C. Meinecke, F. Király, K. Müller, Finding Stationary Subspaces in Multivariate Time Series
Physical Review Letters, 103:214101, 2009 [bibtex]

F. Biessmann, F. C. Meinecke, A. Gretton, A. Rauch, G. Rainer, N. Logothetis, K. Müller, Temporal Kernel Canonical Correlation Analysis and its Application in Multimodal Neuronal Data Analysis
Machine Learning, 79(1-2):5-27, 2009 [bibtex] [pdf] [url]

J. Conradi, B. Blankertz, M. Tangermann, V. Kunzmann, G. Curio, Brain-Computer Interfacing in Tetraplegic Patients with High Spinal Cord Injury
International Journal of Bioelectromagnetism, 11(2):65-68, 2009 [bibtex] [pdf] [url]

S. Fazli, F. Popescu, M. Danóczy, B. Blankertz, K. Müller, C. Grozea, Subject-independent mental state classification in single trials
Neural networks : the official journal of the International Neural Network Society, 22(9):1305-1312, 2009 [bibtex] [url]

K. Hansen, S. Mika, T. Schroeter, A. Sutter, A. t. Laak, T. Steger-Hartmann, N. Heinrich, K. Müller, Benchmark Data Set for in Silico Prediction of Ames Mutagenicity
J. Chem. Inf. Model., 49(9):2077-2081, 2009 [bibtex] [url]

K. Hansen, F. Rathke, T. Schroeter, G. Rast, T. Fox, J. M. Kriegl, S. Mika, BiasCorrection of Regression Models: A Case Study on hERG Inhibition
Journal of Chemical Information and Modelling, 49(6):1486-1496, 2009 [bibtex] [url]

S. Lemm, K. Müller, G. Curio, A Generalized Framework for Quantifying the Dynamics of EEG Event-Related Desynchronization, Open Access
PLoS Comput Biol, Public Library of Science, 5(8), 2009 [bibtex] [url]

C. Sannelli, M. Braun, K. Müller, Improving BCI performance by task-related trial pruning
Neural Networks, 22:1295-1304, 2009 [bibtex] [url]

M. Schreuder, M. Tangermann, B. Blankertz, Initial results of a high-speed spatial auditory BCI
International Journal of Bioelectromagnetism, 11(2):105-109, 2009 [bibtex] [pdf] [url]

R. Schubert, S. Haufe, F. Blankenburg, A. Villringer, G. Curio, Now you'll feel it - now you won't: EEG rhythms predict the effectiveness of perceptual masking
Journal of cognitive neuroscience, 21(12):2407-2419, 2009 [bibtex] [url]

A. Schwaighofer, T. Schroeter, S. Mika, G. Blanchard, How Wrong Can We Get? A Review of Machine Learning Approaches and Error Bars
Combinatorial Chemistry & High Throughput Screening, 12(5):453-468, 2009 [bibtex] [url]

M. Tangermann, I. Winkler, S. Haufe, B. Blankertz, Classification of Artifactual ICA Components
International Journal of Bioelectromagnetism, 11(2):110-114, 2009 [bibtex] [pdf] [url]

C. Vidaurre, N. Krämer, B. Blankertz, A. Schlögl, Time Domain Parameters as a feature for EEG-based Brain Computer Interfaces
Neural Networks, 22:1313-1319, 2009 [bibtex] [url]

S. Wahl, K. Rieck, P. Laskov, P. Domschitz, K. Müller, Securing IMS against Novel Threats
Bell Labs Technical Journal, 14(1):243-257, 2009 [bibtex] [pdf]

J. Williamson, R. Murray-Smith, B. Blankertz, M. Krauledat, K. Müller, Designing for uncertain, asymmetric control: Interaction design for brain-computer interfaces
Int J Hum Comput Stud, 67(10):827-841, 2009 [bibtex] [url]

Conference papers

P. v. Bünau, F. C. Meinecke, K. Müller, Stationary Subspace Analysis
ICA, 2009 [bibtex] [url]

B. Blankertz, K. Müller, G. Curio, Neuronal Correlates of Emotions in Human-Machine Interaction, Eighteenth Annual Computational Neuroscience Meeting: CNS*2009
BMC Neuroscience 2009, 10:(Suppl 1):P80, 2009 [bibtex] [url]

B. Blankertz, C. Sannelli, S. Halder, E. Hammer, A. Kübler, K. Müller, G. Curio, T. Dickhaus, Predicting BCI Performance to Study BCI Illiteracy
7th NFSI & ICBEM 2009, 2009 [bibtex] [pdf]

B. Blankertz, C. Vidaurre, Towards a Cure for BCI Illiteracy: Machine-Learning Based Co-adaptive Learning, Eighteenth Annual Computational Neuroscience Meeting: CNS*2009
BMC Neuroscience 2009, 10:(Suppl 1):P85, 2009 [bibtex] [url]

T. Dickhaus, C. Sannelli, K. Müller, G. Curio, B. Blankertz, Predicting BCI Performance to Study BCI Illiteracy, Eighteenth Annual Computational Neuroscience Meeting: CNS*2009
BMC Neuroscience 2009, 10:(Suppl 1):P84, 2009 [bibtex] [url]

P. Durka, M. Roszkowski, J. Zygierewicz, S. Haufe, G. Nolte, P. Zwolinski, An open database of EEG data from child epilepsy with postoperational foci localization at eeg.pl/epi, Conference Abstract: Neuroinformatics 2009
Frontiers in Neuroscience, 2009 [bibtex] [url]

S. Fazli, C. Grozea, M. Danóczy, B. Blankertz, F. Popescu, K. Müller, Subject independent EEG-based BCI decoding
Advances in Neural Information Processing Systems 22, MIT Press, 2009 [bibtex]

N. Görnitz, M. Kloft, U. Brefeld, Active and Semi-supervised Data Domain Description
Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 2009 [bibtex] [pdf]

N. Görnitz, M. Kloft, K. Rieck, U. Brefeld, Active Learning for Network Intrusion Detection
Proc. of CCS Workshop on Security and Artificial Intelligence (AISEC), 2009 [bibtex] [pdf]

M. Kawanabe, C. Vidaurre, Improving BCI Performance by Modified Common Spatial Patterns with Robustly Averaged Covariance Matrices
WC2009, 2009 [bibtex]

M. Kawanabe, C. Vidaurre, B. Blankertz, K. Müller, A maxmin approach to optimize spatial filters for EEG single-trial classification
Proceedings of IWANN 09, Part I, LNCS, 2009 [bibtex] [pdf]

M. Kawanabe, C. Vidaurre, S. Schoeller, B. Blankertz, K. Mueller, Robust Common Spatial Filters with a Maxmin Approach
EMBS-Conference, 2009 [bibtex]

M. Kloft, U. Brefeld, S. Sonnenburg, P. Laskov, K. Müller, A. Zien, Efficient and Accurate Lp-Norm Multiple Kernel Learning
Advances in Neural Information Processing Systems 22, MIT Press, 2009 [bibtex] [pdf]

M. Kloft, S. Nakajima, U. Brefeld, Feature Selection for Density Level-Sets
Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 2009 [bibtex] [pdf]

T. Lang, M. Toussaint, Relevance Grounding for Planning in Relational Domains
Proc. of the European Conf. on Machine Learning (ECML), 2009 [bibtex] [pdf]

T. Lang, M. Toussaint, Approximate Inference for Planning in Stochastic Relational Worlds
Proc. of the Int. Conf. on Machine Learning (ICML), 2009 [bibtex] [pdf]

P. Laskov, M. Kloft, A framework for quantitative security analysis of machine learning
Proceedings of the 2nd ACM Workshop on Security and Artificial Intelligence (AISEC), 2009 [bibtex] [pdf]

F. C. Meinecke, P. v. Bünau, M. Kawanabe, K. Müller, Learning invariances with Stationary Subspace Analysis
Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on, 2009 [bibtex]

S. Nakajima, A. Binder, C. Müller, W. Wojcikiewicz, M. Kloft, U. Brefeld, K. Müller, M. Kawanabe, Multiple Kernel Learning for Object Classification
Proceedings of the 12th Workshop on Information-based Induction Sciences, 2009 [bibtex] [pdf]

L. Ramsey, M. Tangermann, S. Haufe, B. Blankertz, Practicing fast-decision BCI using a "goalkeeper" paradigm, Eighteenth Annual Computational Neuroscience Meeting: CNS*2009
BMC Neuroscience 2009, 10:(Suppl 1):P69, 2009 [bibtex] [pdf] [url]

K. Rieck, P. Laskov, Visualization and Explanation of Payload-Based Anomaly Detection
Proc. of European Conference on Computer Network Defense (EC2ND), 2009 [bibtex] [pdf]

M. Tangermann, M. Krauledat, K. Grzeska, M. Sagebaum, B. Blankertz, C. Vidaurre, K. Müller, Playing Pinball with non-invasive BCI
Advances in Neural Information Processing Systems 21, December 8-11, 2008, MIT Press, 2009 [bibtex] [pdf]

Technical reports

R. Jenssen, M. Kloft, A. Zien, S. Sonnenburg, K. Müller, A Multi-Class Support Vector Machine Based on Scatter Criteria
Technische Universität Berlin, 2009 [bibtex] [url]

K. Rieck, P. Trinius, C. Willems, T. Holz, Automatic Analysis of Malware Behavior using Machine Learning
Berlin Institute of Technology, 2009 [bibtex]

2008

Journal papers

A. Ben-Hur, C. S. Ong, S. Sonnenburg, B. Schölkopf, G. Rätsch, Support Vector Machines and Kernels for Computational Biology
PLoS Computational Biology, 4, 2008 [bibtex] [url]

B. Blankertz, F. Losch, M. Krauledat, G. Dornhege, G. Curio, K. Müller, The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects
IEEE transactions on bio-medical engineering, 55(10):2452-2462, 2008 [bibtex] [pdf] [url]

B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, K. Müller, Optimizing Spatial Filters for Robust EEG Single-Trial Analysis
IEEE Signal Processing Magazine, 25(1):41-56, 2008 [bibtex] [pdf] [url]

M. L. Braun, J. Buhmann, K. Müller, On Relevant Dimensions in Kernel Feature Spaces
Journal of Machine Learning Research, 9:1875-1908, 2008 [bibtex]

S. Haufe, V. V. Nikulin, A. Ziehe, K. Müller, G. Nolte, Combining sparsity and rotational invariance in EEG/MEG source reconstruction
NeuroImage, 42(2):726-738, 2008 [bibtex] [pdf] [url]

M. Krauledat, M. Tangermann, B. Blankertz, K. Müller, Towards Zero Training for Brain-Computer Interfacing, Open Access
PloS one, Public Library of Science, 3(8):e2967, 2008 [bibtex] [pdf] [url]

K. Müller, M. Tangermann, G. Dornhege, M. Krauledat, G. Curio, B. Blankertz, Machine learning for real-time single-trial EEG-analysis: From brain-computer interfacing to mental state monitoring
Journal of neuroscience methods, 167(1):82-90, 2008 [bibtex] [pdf] [url]

L. Marzetti, C. D. Gratta, G. Nolte, Understanding brain connectivity from EEG data by identifying systems composed of interacting sources
NeuroImage, 42(1):87 - 98, 2008 [bibtex]

A. Nijholt, D. Tan, G. Pfurtscheller, C. Brunner, J. Millán, B. Allison, B. Grainmann, F. Popescu, B. Blankertz, K. Müller, Brain-Computer Interfacing for Intelligent Systems
IEEE Intelligent Systems, 23(3):72-79, 2008 [bibtex] [pdf] [url]

G. Nolte, A. Ziehe, V. Nikulin, A. Schlögl, N. Krämer, T. Brismar, K. Müller, Robustly Estimating the Flow Direction of Information in Complex Physical Systems
Physical Review Letters, 100:234101, 2008 [bibtex] [pdf] [url]

K. Rieck, P. Laskov, Linear-Time Computation of Similarity Measures for Sequential Data
Journal of Machine Learning Research, Microtome Publishing, 9(Jan):23-48, 2008 [bibtex] [pdf]

A. Schwaighofer, T. Schroeter, S. Mika, K. Hansen, A. t. Laak, P. Lienau, A. Reichel, N. Heinrich, K. Müller, A Probabilistic Approach to Classifying Metabolic Stability
Journal of Chemical Information and Modelling, 48(4):785-796, 2008 [bibtex] [pdf] [url]

S. Sonnenburg, A. Zien, P. Philips, G. Rätsch, POIMs: Positional Oligomer Importance Matrices - Understanding Support Vector Machine Based Signal Detectors, (received the Best Student Paper Award at ISMB08)
Bioinformatics, 2008 [bibtex]

M. Sugiyama, T. Suzuki, S. Nakajima, H. Kashima, P. v. Bünau, M. Kawanabe, Direct Importance Estimation for Covariate Shift Adaptation
Annals of the Institute of Statistical Mathematics, 60(4):699-746, 2008 [bibtex]

Book chapters

B. Blankertz, M. Tangermann, F. Popescu, M. Krauledat, S. Fazli, M. Danóczy, G. Curio, K. Müller, The Berlin Brain-Computer Interface
WCCI 2008 Plenary/Invited Lectures, Springer, LNCS, 5050:79-101, 2008 [bibtex] [pdf] [url]

P. Laskov, K. Rieck, K. Müller, Machine Learning for Intrusion Detection
Mining Massive Data Sets for Security, IOS press, 2008 [bibtex]

H. Purwins, B. Blankertz, K. Obermayer, Toroidal Models in Tonal Theory and Pitch- Class Analysis
Tonal Theory for the Digital Age, Computing in Musicology, Center for Computer Assisted Research in the Humanities and MIT Press, 15:74-103, 2008 [bibtex] [pdf]

Conference papers

B. Blankertz, M. Kawanabe, R. Tomioka, F. Hohlefeld, V. Nikulin, K. Müller, Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing
Advances in Neural Information Processing Systems 20, MIT Press, 2008 [bibtex] [pdf]

L. Braun, F. Dressler, T. Holz, E. Kirda, J. Kohlrausch, C. K. Limmer, K. Rieck, J. P. Sterbenz, Requirements for Network Monitoring from an IDS Perspective
Perspectives Workshop: Network Attack Detection and Defense (Dagstuhl Seminar Proceedings), 2008 [bibtex]

P. Düssel, C. Gehl, P. Laskov, K. Rieck, Incorporation of Application Layer Protocol Syntax into Anomaly Detection
Proc. of International Conference on Information Systems Security (ICISS), 2008 [bibtex] [pdf]

M. Dacier, H. Debar, T. Holz, E. Kirda, J. Kohlrausch, C. Kruegel, K. Rieck, J. P. Sterbenz, Attack Taxonomy
Perspectives Workshop: Network Attack Detection and Defense (Dagstuhl Seminar Proceedings), 2008 [bibtex]

S. Fazli, M. Danóczy, M. Kawanabe, F. Popescu, Asynchronous, adaptive BCI using movement imagination training and rest-state inference
IASTED's Proceedings on Artificial Intelligence and Applications 2008, 2008 [bibtex] [url]

V. Franc, S. Sonnenburg, OCAS Optimized cutting plane algorithm for support vector machines
Proceedings of the 25nd International Machine Learning Conference, ACM Press, 2008 [bibtex]

S. Haufe, V. V. Nikulin, A. Ziehe, K. Müller, G. Nolte, Estimating vector fields using sparse basis field expansions
Advances in Neural Information Processing Systems 21, MIT Press, 2008 [bibtex] [pdf]

T. Holz, C. Gorecki, K. Rieck, F. C. Freiling, Measuring and Detecting Fast-Flux Service Networks
15th Annual Network & Distributed System Security Symposium (NDSS), 2008 [bibtex] [pdf]

M. Kloft, U. Brefeld, P. Düssel, C. Gehl, P. Laskov, Automatic feature selection for anomaly detection
AISec, ACM, 2008 [bibtex] [pdf]

M. Kloft, U. Brefeld, P. Laskov, S. Sonnenburg, Non-Sparse Multiple Kernel Learning
Proc. of the NIPS Workshop on Kernel Learning: Automatic Selection of Kernels, 2008 [bibtex] [pdf]

T. Krueger, C. Gehl, K. Rieck, P. Laskov, An Architecture for Inline Anomaly Detection
Proc. of European Conference on Computer Network Defense (EC2ND), 2008 [bibtex] [pdf]

K. Rieck, T. Holz, C. Willems, P. Düssel, P. Laskov, Learning and Classification of Malware Behavior
Detection of Intrusions and Malware, and Vulnerability Assessment, Proc. of 5th DIMVA Conference, 2008 [bibtex] [pdf]

K. Rieck, S. Wahl, P. Laskov, P. Domschitz, K. Müller, A Self-Learning System for Detection of Anomalous SIP Messages
Principles, Systems and Applications of IP Telecommunications (IPTCOMM), Second International Conference, LNCS, 2008 [bibtex] [pdf]

M. Schubert, J. Kohlmorgen, Hierarchical Feature Extraction for Compact Representation and Classification of Datasets
ICONIP 2007, Springer, LNCS 4984, 2008 [bibtex] [pdf]

R. Schubert, M. Tangermann, S. Haufe, C. Sannelli, M. Simon, E. A. Schmidt, W. E. Kincses, G. Curio, Parieto-occipital alpha power indexes distraction during simulated car driving, Abstracts of the 14th World Congress of Psychophysiology
International Journal of Psychophysiology, 69(3), 2008 [bibtex] [pdf] [url]

C. Vidaurre, A. Schlögl, Comparison of adaptive features with linear discriminant classifier for Brain Computer Interfaces
Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2008, 2008 [bibtex]

Technical reports

K. Rieck, U. Brefeld, T. Krueger, Approximate Kernels for Trees
Fraunhofer Institute FIRST, 2008 [bibtex] [pdf]

PhD theses

M. Krauledat, Analysis of Nonstationarities in EEG signals for improving Brain-Computer Interface performance
Technische Universität Berlin, Fakultät IV - Elektrotechnik und Informatik, 2008 [bibtex] [pdf]

2007

Books

G. Dornhege, J. d. R. Millán, T. Hinterberger, D. McFarland, K. Müller, (Eds.), Toward Brain-Computer Interfacing
MIT Press, 2007 [bibtex]

Journal papers

G. Blanchard, O. Bousquet, L. Zwald, Statistical properties of kernel components analysis
Machine Learning, 66(2-3):259-294, 2007 [bibtex] [pdf] [url]

G. Blanchard, C. Schäfer, Y. Rozenholc, K. Müller, Optimal dyadic decision trees
Machine Learning, 66(2-3):209-241, 2007 [bibtex] [pdf] [url]

B. Blankertz, G. Dornhege, M. Krauledat, K. Müller, G. Curio, The non-invasive Berlin Brain-Computer Interface: Fast Acquisition of Effective Performance in Untrained Subjects
NeuroImage, 37(2):539-550, 2007 [bibtex] [pdf] [url]

M. Kawanabe, M. Sugiyama, G. Blanchard, K. Müller, A new algorithm of non-Gaussian component analysis with radial kernel functions
Annals of the Institute of Statistical Mathematics, 59(1):57-75, 2007 [bibtex]

M. Krauledat, G. Dornhege, B. Blankertz, K. Müller, Robustifying EEG data analysis by removing outliers
Chaos and Complexity Letters, 2(3):259-274, 2007 [bibtex] [pdf]

R. Krepki, B. Blankertz, G. Curio, K. Müller, The Berlin Brain-Computer Interface (BBCI): towards a new communication channel for online control in gaming applications
Journal of Multimedia Tools and Applications, 33(1):73-90, 2007 [bibtex] [pdf] [url]

R. Krepki, G. Curio, B. Blankertz, K. Müller, Berlin Brain-Computer Interface - the HCI Communication Channel for Discovery, Special Issue on Ambient Intelligence
Int J Hum Comp Studies, 65:460-477, 2007 [bibtex] [pdf]

V. V. Nikulin, K. Linkenkaer-Hansen, G. Nolte, S. Lemm, K. Müller, R. J. Ilmoniemi, G. Curio, A novel mechanism for evoked responses in human brain
The European journal of neuroscience, 25:3146-54, 2007 [bibtex] [pdf]

F. Popescu, S. Fazli, Y. Badower, B. Blankertz, K. Müller, Single Trial Classification of Motor Imagination Using 6 Dry EEG Electrodes, Open Access
PloS one, 2(7):e637, 2007 [bibtex] [pdf] [url]

G. Rätsch, S. Sonnenburg, J. Srinivasan, H. Witte, R. Sommer, K. Müller, B. Schölkopf, Improving the C. elegans genome annotation using machine learning
PLoS Computational Biology, 3:e20, 2007 [bibtex] [pdf]

K. Rieck, P. Laskov, Language Models for Detection of Unknown Attacks in Network Traffic
Journal in Computer Virology, Springer, 2(4):243-256, 2007 [bibtex] [pdf]

T. Schroeter, A. Schwaighofer, S. Mika, A. t. Laak, D. Sülzle, U. Ganzer, N. Heinrich, K. Müller, Predicting Lipophilicity of Drug Discovery Molecules using Gaussian Process Models
ChemMedChem, 2(9):1265-1267, 2007 [bibtex] [pdf] [url]

T. Schroeter, A. Schwaighofer, S. Mika, A. T. Laak, D. Suelzle, U. Ganzer, N. Heinrich, K. Müller, Estimating the Domain of Applicability for Machine Learning QSAR RModels: A Study on Aqueous Solubility of Drug Discovery Molecules
Journal of Computer Aided Molecular Design - special issue on "ADME and Physical Properties", 21(9):485-498, 2007 [bibtex] [pdf] [url]

T. Schroeter, A. Schwaighofer, S. Mika, A. T. Laak, D. Suelzle, U. Ganzer, N. Heinrich, K. Müller, Machine Learning Models for Lipophilicity and their Domain of Applicability
Mol. Pharm., 4(4):524-538, 2007 [bibtex] [pdf] [url]

T. Schroeter, A. Schwaighofer, S. Mika, A. T. Laak, D. Suelzle, U. Ganzer, N. Heinrich, K. Müller, Estimating the Domain of Applicability for Machine Learning QSAR RModels: A Study on Aqueous Solubility of Drug Discovery Molecules
Journal of Computer Aided Molecular Design - regular issue, 21(12):651-664, 2007 [bibtex] [pdf] [url]

A. Schwaighofer, T. Schroeter, S. Mika, J. Laub, A. t. Laak, D. Sülzle, U. Ganzer, N. Heinrich, K. Müller, Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach
Journal of Chemical Information and Modelling, 47(2):407-424, 2007 [bibtex] [pdf] [url]

S. Sonnenburg, M. Braun, C. S. Ong, S. Bengio, L. Bottou, G. H. LeCun, K. Müller, F. Pereira, C. E. Rasmussen, G. Rätsch, B. Schölkopf, A. Smola, P. Vincent, J. Weston, R. Williamson, The Need for Open Source Software in Machine Learning
Journal of Machine Learning Research, 8:2443-2466, 2007 [bibtex] [pdf]

S. Sonnenburg, G. Schweikert, P. Philips, J. Behr, G. Rätsch, Accurate Splice Site Prediction
BMC Bioinformatics, Special Issue from NIPS workshop on New Problems and Methods in Computational Biology Whistler, Canada, 18 December 2006, 8:(Suppl. 10):S7, 2007 [bibtex] [pdf]

M. Sugiyama, M. Krauledat, K. Müller, Covariate Shift Adaptation by Importance Weighted Cross Validation
Journal of Machine Learning Research, 8:1027-1061, 2007 [bibtex] [pdf]

Book chapters

B. Blankertz, G. Dornhege, M. Krauledat, V. Kunzmann, F. Losch, G. Curio, K. Müller, The Berlin Brain-Computer Interface: Machine-Learning based Detection of User Specific Brain States
Toward Brain-Computer Interfacing, MIT press, 2007 [bibtex]

G. Dornhege, M. Krauledat, K. Müller, B. Blankertz, General signal processing and machine learning tools for BCI
Toward Brain-Computer Interfacing, MIT Press, 2007 [bibtex]

A. Kübler, K. Müller, An introducton to brain computer interfacing
Toward Brain-Computer Interfacing, MIT press, 2007 [bibtex]

J. Kohlmorgen, G. Dornhege, M. Braun, B. Blankertz, K. Müller, G. Curio, K. Hagemann, A. Bruns, M. Schrauf, W. Kincses, Improving human performance in a real operating environment through real-time mental workload detection
Toward Brain-Computer Interfacing, MIT press, 2007 [bibtex] [pdf]

M. Krauledat, P. Shenoy, B. Blankertz, R. P. Rao, K. Müller, Adaptation in CSP-based BCI systems
Toward Brain-Computer Interfacing, MIT Press, 2007 [bibtex]

S. Sonnenburg, G. Rätsch, K. Rieck, Large Scale Learning with String Kernels
Large Scale Kernel Machines, MIT Press, 2007 [bibtex]

Conference papers

S. Arlot, G. Blanchard, E. Roquain, Resampling-based confidence regions and multiple tests for a correlated random vector
Proceedings of the 20th. conference on learning theory (COLT 2007), Springer Lecture Notes on Computer Science, 4539:127-141, 2007 [bibtex] [pdf]

G. Blanchard, F. Fleuret, Occam's Hammer
Proceedings of the 20th. conference on learning theory (COLT 2007), Springer Lecture Notes on Computer Science, 4539:112-126, 2007 [bibtex] [pdf]

B. Blankertz, M. Krauledat, G. Dornhege, J. Williamson, R. Murray-Smith, K. Müller, A Note on Brain Actuated Spelling with the Berlin Brain-Computer Interface
Universal Access in HCI, Part II, HCII 2007, Springer, LNCS, 4555:759-768, 2007 [bibtex] [pdf]

M. L. Braun, J. Buhmann, K. Müller, Denoising and Dimension Reduction in Feature Space, accepted
Advances in Neural Inf. Proc. Systems (NIPS 20), 2007 [bibtex]

M. Krauledat, M. Schröder, B. Blankertz, K. Müller, Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach
Advances in Neural Information Processing Systems 19, MIT Press, 2007 [bibtex] [pdf]

S. Lemm, C. Schäfer, G. Curio, Aggregating Classification Accuracy across Time: Application to Single Trial EEG
Advances in Neural Inf. Proc. Systems (NIPS 06), MIT press, 19:825-832, 2007 [bibtex] [pdf]

K. Müller, M. Krauledat, G. Dornhege, G. Curio, B. Blankertz, Machine Learning and Applications for Brain-Computer Interfacing
Human Interface, Part I, HCII 2007, Springer, LNCS, 4557:705-714, 2007 [bibtex]

G. Rätsch, S. Sonnenburg, Large Scale Hidden Semi-Markov SVMs
Advances in Neural Information Processing Systems 19, MIT Press, 2007 [bibtex] [pdf] [ps]

K. Rieck, P. Laskov, S. Sonnenburg, Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees
Advances in Neural Information Processing Systems 19, MIT Press, 2007 [bibtex] [pdf]

E. A. Schmidt, W. E. Kincses, M. Schrauf, S. Haufe, R. Schubert, G. Curio, Assessing Drivers' Vigilance State During Monotonous Driving
Proceedings of the Fourth International Symposium on Human Factors in Driving Assessment, Training, and Vehicle Design, 2007 [bibtex] [pdf]

R. Tomioka, K. Aihara, Classifying Matrices with a Spectral Regularization
ICML '07: Proceedings of the 24th international conference on Machine learning, ACM Press, 2007 [bibtex] [pdf]

R. Tomioka, K. Aihara, K. Müller, Logistic Regression for Single Trial EEG Classification
Advances in Neural Information Processing Systems 19, MIT Press, 2007 [bibtex] [pdf]

Technical reports

V. Franc, S. Sonnenburg, Optimized cutting plane algorithm for support vector machines
Fraunhofer Institute FIRST, 2007 [bibtex] [pdf]

A. Zien, P. Philips, S. Sonnenburg, Computing Positional Oligomer Importance Matrices (POIMs)
Fraunhofer Institute FIRST, 2007 [bibtex] [pdf]

PhD theses

S. Lemm, Analyses of single trial encephalogram data
Technical University of Berlin, 2007 [bibtex] [pdf]

2006

Journal papers

G. Blanchard, M. Sugiyama, M. Kawanabe, V. Spokoiny, K. Müller, In search of non-Gaussian components of a high-dimensional distribution
Journal of Machine Learning Research, 7:247-282, 2006 [bibtex] [pdf]

B. Blankertz, G. Dornhege, M. Krauledat, K. Müller, V. Kunzmann, F. Losch, G. Curio, The Berlin Brain-Computer Interface: EEG-based communication without subject training
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 14(2):147-152, 2006 [bibtex] [pdf] [url]

B. Blankertz, G. Dornhege, S. Lemm, M. Krauledat, G. Curio, K. Müller, The Berlin Brain-Computer Interface: Machine Learning Based Detection of User Specific Brain States
J Universal Computer Sci, 12(6):581-607, 2006 [bibtex] [pdf] [url]

B. Blankertz, K. Müller, D. Krusienski, G. Schalk, J. R. Wolpaw, A. Schlögl, G. Pfurtscheller, J. d. R. Millán, M. Schröder, N. Birbaumer, The BCI Competition III: Validating Alternative Approachs to Actual BCI Problems
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 14(2):153-159, 2006 [bibtex] [pdf] [url]

M. L. Braun, Accurate bounds for the eigenvalues of the kernel matrix
Journal of Machine Learning Research, 7:2303-2328, 2006 [bibtex]

S. CJ, J. BF, N. G, B. M, S. P, Small-World Networks and Functional Connectivity in Alzheimer's Disease
Cereb Cortex, 2006 [bibtex]

G. Dornhege, B. Blankertz, M. Krauledat, F. Losch, G. Curio, K. Müller, Combined optimization of spatial and temporal filters for improving Brain-Computer Interfacing
IEEE transactions on bio-medical engineering, 53(11):2274-2281, 2006 [bibtex] [pdf] [url]

S. Harmeling, G. Dornhege, D. Tax, F. C. Meinecke, K. Müller, From outliers to prototypes: ordering data
Neurocomputing, 69(13-15):1608-1618, 2006 [bibtex]

N. J. Hill, T. N. Lal, M. Schröder, T. Hinterberger, B. Wilhelm, F. Nijboer, U. Mochty, G. Widman, C. Elger, B. Schölkopf, A. Kübler, N. Birbaumer, Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects
IEEE Transactions on Neural Systems and Rehabilitation Engineering, IEEE, 14(2):183-186, 2006 [bibtex]

P. Laskov, C. Gehl, S. Krüger, K. Müller, Incremental Support Vector Learning: Analysis, Implementation and Applications
Journal of Machine Learning Research, 7:1909-1936, 2006 [bibtex] [pdf]

J. Laub, V. Roth, J. Buhmann, K. Müller, On the information and representation of non-Euclidean pairwise data
Pattern Recognition, 39(10):1815-1826, 2006 [bibtex]

S. Lemm, G. Curio, Y. Hlushchuk, K. Müller, Enhancing the Signal to Noise Ratio of ICA-based Extracted ERPs
IEEE transactions on bio-medical engineering, 53(4):601-607, 2006 [bibtex]

K. Müller, B. Blankertz, Toward noninvasive Brain-Computer Interfaces
IEEE Signal Processing Magazine, 23(5):125-128, 2006 [bibtex] [pdf] [url]

D. J. McFarland, C. W. Anderson, K. Müller, A. Schlögl, D. J. Krusienski, BCI Meeting 2005-workshop on BCI signal processing: feature extraction and translation
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 14:135-138, 2006 [bibtex] [url]

G. Nolte, F. C. Meinecke, A. Ziehe, K. Müller, Identifying interactions in mixed and noisy complex systems
Physical Review E, 73:051913, 2006 [bibtex]

G. Rätsch, S. Sonnenburg, C. Schäfer, Learning Interpretable SVMs for Biological Sequence Classification
BMC Bioinformatics, Special Issue from NIPS workshop on New Problems and Methods in Computational Biology Whistler, Canada, 18 December 2004, 7:(Suppl. 1:S9), 2006 [bibtex]

R. Schubert, F. Blankenburg, S. Lemm, A. Villringer, G. Curio, Now you feel it - now you don't: ERP correlates of somatosensory awareness
Psychophysiology, 43(1):31-40, 2006 [bibtex]

P. Shenoy, M. Krauledat, B. Blankertz, R. P. Rao, K. Müller, Towards Adaptive Classification for BCI
Journal of neural engineering, 3(1):R13-R23, 2006 [bibtex] [pdf] [url]

S. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf, Large Scale Multiple Kernel Learning
Journal of Machine Learning Research, 7:1531-1565, 2006 [bibtex] [pdf]

S. Sonnenburg, A. Zien, G. Rätsch, ARTS: Accurate Recognition of Transcription Starts in Human
Bioinformatics, 22(14):e472-e480, 2006 [bibtex] [pdf]

Book chapters

N. Hill, T. N. Lal, M. Schröder, T. Hinterberger, G. Widman, C. E. Elger, B. Schölkopf, N. Birbaumer, Classifying Event-Related Desynchronization in EEG, ECoG and MEG Signals
Pattern Recognition, Springer Berlin Heidelberg, Lecture Notes in Computer Science, 4174:404-413, 2006 [bibtex] [url]

Conference papers

G. Blanchard, M. Sugiyama, M. Kawanabe, V. Spokoiny, K. Müller, Non-Gaussian component analysis: a semi-parametric framework for linear dimension reduction
Advances in Neural Inf. Proc. Systems (NIPS 05), 18, 2006 [bibtex] [pdf]

B. Blankertz, G. Dornhege, M. Krauledat, M. Schröder, J. Williamson, R. Murray-Smith, K. Müller, The Berlin Brain-Computer Interface presents the novel mental typewriter Hex-o-Spell
Proceedings of the 3rd International Brain-Computer Interface Workshop and Training Course 2006, Verlag der TU Graz, 2006 [bibtex] [pdf]

M. L. Braun, T. Lange, J. Buhmann, Model Selection in Kernel Methods based on a Spectral Analysis of Label Information
Proc. DAGM, LNCS, 4174:344-353, 2006 [bibtex] [pdf]

G. Dornhege, B. Blankertz, M. Krauledat, F. Losch, G. Curio, K. Müller, Optimizing spatio-temporal filters for improving Brain-Computer Interfacing
Advances in Neural Inf. Proc. Systems (NIPS 05), MIT Press, 18:315-322, 2006 [bibtex]

F. Fleuret, G. Blanchard, Pattern recognition from one example via chopping
Advances in Neural Inf. Proc. Systems (NIPS 05), 18, 2006 [bibtex]

M. Kawanabe, G. Blanchard, M. Sugiyama, V. Spokoiny, K. Müller, A novel dimension reduction procedure for searching non-Gaussian subspaces, accepted
Proc. of ICA2006, 2006 [bibtex]

M. Kawanabe, F. Theis, Estimating non-Gaussian subspaces by characteristic functions, accepted
Proc. of ICA2006, 2006 [bibtex]

M. Krauledat, B. Blankertz, G. Dornhege, M. Schröder, G. Curio, K. Müller, On-line differentiation of neuroelectric activities: algorithms and applications
Proceedings of the 28th Annual International Conference IEEE EMBS on Biomedicine, 2006 [bibtex] [pdf]

K. Müller, M. Krauledat, G. Dornhege, S. Jähnichen, G. Curio, B. Blankertz, A note on the Berlin Brain-Computer Interface
Human Interaction with Machines: Proceedings of the 6th International Workshop held at the Shanghai Jiao Tong University, 2006 [bibtex]

G. Nolte, A. Ziehe, F. C. Meinecke, K. Müller, Analyzing Coupled Brain Sources: Distinguishing True from Spurious Interaction, accepted
Advances in Neural Inf. Proc. Systems (NIPS 05), 18, 2006 [bibtex]

F. Popescu, Y. Badower, S. Fazli, G. Dornhege, K. Müller, EEG-based control of reaching to visual targets
Dynamical Principles for neuroscience and intelligent biomimetic devices - Abstracts of the EPFL-LATSIS Symposium 2006, 2006 [bibtex]

K. Rieck, P. Laskov, Detecting Unknown Network Attacks using Language Models
Detection of Intrusions and Malware, and Vulnerability Assessment, Proc. of 3rd DIMVA Conference, 2006 [bibtex] [pdf]

K. Rieck, P. Laskov, K. Müller, Efficient Algorithms for Similarity Measures over Sequential Data: A Look beyond Kernels
Pattern Recognition, Proc. of 28th DAGM Symposium, 2006 [bibtex] [pdf]

S. Sonnenburg, G. Rätsch, C. Schäfer, A General and Efficient Multiple Kernel Learning Algorithm
Advances in Neural Information Processing Systems 18, MIT Press, 2006 [bibtex] [pdf] [ps]

M. Sugiyama, B. Blankertz, M. Krauledat, G. Dornhege, K. Müller, Importance-Weighted Cross-Validation for Covariate Shift
Proc. DAGM, LNCS 4174, Springer-Verlag, 2006 [bibtex] [pdf]

M. Sugiyama, M. Kawanabe, G. Blanchard, V. Spokoiny, K. Müller, Obtaining the best linear unbiased estimator of noisy signals by non-Gaussian component analysis
Proc. of ICASSP2006, 2006 [bibtex]

F. Theis, M. Kawanabe, Uniqueness of non-Gaussian subspace analysis, accepted
Proc. of ICA2006, 2006 [bibtex]

R. Tomioka, G. Dornhege, G. Nolte, K. Aihara, K. Müller, Optimizing Spectral Filters for Single Trial EEG Classification
Proc. DAGM, LNCS 4174, Springer-Verlag, 2006 [bibtex] [pdf]

R. Tomioka, J. Hill, B. Blankertz, K. Aihara, Adapting Spatial Filtering Methods for Nonstationary BCIs
Proceedings of 2006 Workshop on Information-Based Induction Sciences (IBIS2006), 2006 [bibtex] [pdf]

L. Zwald, G. Blanchard, On the convergence of eigenspaces in kernel principal components analysis
Advances in Neural Inf. Proc. Systems (NIPS 05), 18, 2006 [bibtex] [pdf]

Technical reports

R. Tomioka, G. Dornhege, G. Nolte, B. Blankertz, K. Aihara, K. Müller, Spectrally Weighted Common Spatial Pattern Algorithm for Single Trial EEG Classification
Dept. of Mathematical Engineering, The University of Tokyo, 2006 [bibtex] [pdf]

PhD theses

G. Dornhege, Increasing Information Transfer Rates for Brain-Computer Interfacing
University of Potsdam, 2006 [bibtex] [pdf]

2005

Journal papers

G. Blanchard, D. Geman, Hierarchical testing designs for pattern recognition
Annals of Statistics, 33(3):1155-1202, 2005 [bibtex]

M. Kawanabe, K. Müller, Estimating functions for blind separation when sources have variance dependencies
Journal of Machine Learning Research, 6:453-482, 2005 [bibtex]

S. Lemm, B. Blankertz, G. Curio, K. Müller, Spatio-Spectral Filters for Improving Classification of Single Trial EEG
IEEE transactions on bio-medical engineering, 52(9):1541-1548, 2005 [bibtex] [pdf] [url]

K. Müller, G. Rätsch, S. Sonnenburg, S. Mika, M. Grimm, N. Heinrich, Classifying 'Drug-likeness' with Kernel-Based Learning Methods
J. Chem. Inf. Model, 45:249-253, 2005 [bibtex] [pdf]

F. C. Meinecke, S. Harmeling, K. Müller, Inlier-based ICA with an application to super-imposed images
International Journal of Imaging Systems and Technology, Wiley Subscription Services, Inc., A Wiley Company, 15(1):48-55, 2005 [bibtex] [url]

F. C. Meinecke, A. Ziehe, J. Kurths, K. Müller, Measuring Phase Synchronization of Superimposed Signals
Physical Review Letters, 94(8):084102, 2005 [bibtex] [pdf]

G. Nolte, G. Dassios, Analytic expansion of the EEG lead field for realistic volume conductors
Physics in Medicine and Biology, 50(16):3807-23, 2005 [bibtex]

G. Rätsch, S. Sonnenburg, B. Schölkopf, RASE: Recognition of Alternatively Spliced Exons in C. elegans
Bioinformatics, 21:i369-i377, 2005 [bibtex] [pdf]

C. Schäfer, J. Schräpler, K. Müller, G. G. Wagner, Automatic Identification of Faked and Fraudulent Interviews in the German SOEP
Schmollers Jahrbuch,Duncker & Humblot, Berlin, 125:183-193, 2005 [bibtex]

S. Sugiyama, K. Müller, Input-Dependent Estimation of Generalization Error under Covariate Shift
Statistics and Decisions, 23(4):249-279, 2005 [bibtex] [pdf]

Conference papers

G. Blanchard, P. Massart, R. Vert, L. Zwald, Kernel projection machine: a new tool for pattern recognition
Advances in Neural Inf. Proc. Systems (NIPS 04), 2005 [bibtex]

M. Kawanabe, Linear dimension reduction based on the fourth-order cumulant tensor
Proc. of Artifical Neural Networks - ICANN 2005, 2005 [bibtex]

P. Laskov, P. Düssel, C. Schäfer, K. Rieck, Learning intrusion detection: supervised or unsupervised?
Image Analysis and Processing, Proc. of 13th ICIAP Conference, 2005 [bibtex] [pdf]

P. Laskov, K. Rieck, C. Schäfer, K. Müller, Visualization of anomaly detection using prediction sensitivity
Proc. of Conference "Sicherheit, Schutz und Zuverlässigkeit" (SICHERHEIT), 2005 [bibtex] [pdf]

C. Schäfer, J. Laub, GfKl 2004 contest: Annealed k-means clustering and Decision Trees
Proceedings of the 28th annual conference of the Gesellschaft für Klassifikation, 2005 [bibtex]

C. Schäfer, S. Lemm, G. Curio, Binary on-line classification based on temporally integrated information
Proceedings of the 28th annual conference of the Gesellschaft für Klassifikation, 2005 [bibtex]

A. Schwaighofer, V. Tresp, K. Yu, Learning Gaussian Process Kernels via Hierarchical Bayes
Advances in Neural Inf. Proc. Systems (NIPS 2004), MIT Press, 2005 [bibtex]

S. Sonnenburg, G. Rätsch, C. Schäfer, Learning Interpretable SVMs for Biological Sequence Classification
RECOMB 2005, LNBI 3500, Springer-Verlag Berlin Heidelberg, 2005 [bibtex] [pdf] [ps]

S. Sonnenburg, G. Rätsch, B. Schölkopf, Large Scale Genomic Sequence SVM Classifiers
Proceedings of the International Conference on MachineLearning, ICML, 2005 [bibtex] [pdf] [ps]

K. Yu, V. Tresp, A. Schwaighofer, Learning Gaussian Processes from Multiple Tasks
Machine Learning: Proceedings of the 22nd International Conference (ICML 2005), Morgan Kaufman, 2005 [bibtex]

Technical reports

B. Blankertz, G. Dornhege, M. Krauledat, K. Müller, G. Curio, The Berlin Brain-Computer Interface: Report from the Feedback Sessions
Fraunhofer FIRST, 2005 [bibtex] [pdf]

PhD theses

M. L. Braun, Spectral Properties of the Kernel Matrix and their Relation to Kernel Methods in Machine Learning
University of Bonn, 2005 [bibtex] [url]

2004

Journal papers

G. Blanchard, Different paradigms for choosing sequential reweighting algorithms
Neural Computation, 16:811-836, 2004 [bibtex] [pdf]

G. Blanchard, Un algorithme accelere d'echantillonnage Bayesien pour le modele CART
Revue d'Intelligence artificielle, 18(3):383-410, 2004 [bibtex]

G. Blanchard, B. Blankertz, BCI Competition 2003 - Data Set IIa: Spatial Patterns of Self-Controlled Brain Rhythm Modulations
IEEE transactions on bio-medical engineering, 51(6):1062-1066, 2004 [bibtex] [pdf] [url]

B. Blankertz, K. Müller, G. Curio, T. M. Vaughan, G. Schalk, J. R. Wolpaw, A. Schlögl, C. Neuper, G. Pfurtscheller, T. Hinterberger, M. Schröder, N. Birbaumer, The BCI Competition 2003: Progress and Perspectives in Detection and Discrimination of EEG Single Trials
IEEE transactions on bio-medical engineering, 51(6):1044-1051, 2004 [bibtex] [pdf] [url]

M. Dejori, A. Schwaighofer, V. Tresp, M. Stetter, Mining Functional Modules in Genetic Networks with Decomposable Graphical Models
OMICS Journal of Integrative Biology, 8(2):176-188, 2004 [bibtex]

G. Dornhege, B. Blankertz, G. Curio, K. Müller, Boosting bit rates in non-invasive EEG single-trial classifications by feature combination and multi-class paradigms
IEEE transactions on bio-medical engineering, 51(6):993-1002, 2004 [bibtex] [pdf] [url]

S. Harmeling, F. C. Meinecke, K. Müller, Injecting noise for analysing the stability of ICA components
Signal Processing, 84:255-266, 2004 [bibtex]

T. Hinterberger, S. Schmidt, N. Neumann, J. Mellinger, B. Blankertz, G. Curio, N. Birbaumer, Brain-Computer Communication with Slow Cortical Potentials: Methodology and Critical Aspects
IEEE transactions on bio-medical engineering, 51(6):1011-1018, 2004 [bibtex] [pdf] [url]

S. Knabe, S. Mika, M. K.-R., R. G., W. Schruff, Zur Beurteilung des Fraud-Risikos im Rahmen der Abschlussprüfung
Die Wirtschaftsprüfung, 19:1057-1068, 2004 [bibtex]

J. Kohlmorgen, B. Blankertz, Bayesian Classification of Single-Trial Event-Related Potentials in EEG
Int. J. Bif. Chaos, 14(2):719-726, 2004 [bibtex] [pdf]

M. Krauledat, G. Dornhege, B. Blankertz, G. Curio, K. Müller, The Berlin Brain-Computer Interface For Rapid Response
Biomed Tech, 49(1):61-62, 2004 [bibtex] [pdf]

P. Laskov, C. Schäfer, I. Kotenko, K. Müller, Intrusion detection in unlabeled data with quarter-sphere Support Vector Machines (Extended Version)
Praxis der Informationsverarbeitung und Kommunikation, 27:228-236, 2004 [bibtex]

J. Laub, K. Müller, Feature Discovery in Non-Metric Pairwise Data
Journal of Machine Learning, 5(Jul):801-818, 2004 [bibtex]

S. Lemm, C. Schäfer, G. Curio, Probabilistic Modeling of Sensorimotor u-Rhythms for Classification of Imaginary Hand Movements
IEEE transactions on bio-medical engineering, 51(6):1077-1080, 2004 [bibtex] [pdf]

K. Müller, M. Krauledat, G. Dornhege, G. Curio, B. Blankertz, Machine learning techniques for Brain-Computer Interfaces
Biomed Tech, 49(1):11-22, 2004 [bibtex] [pdf]

K. Müller, R. Vigario, F. C. Meinecke, A. Ziehe, Blind Source Separation Techniques For Decomposing Event-Related Brain Signals
International Journal of Bifurcation and Chaos, 14(2):773-791, 2004 [bibtex]

G. Nolte, O. Bai, L. Wheaton, Z. Mari, S. Vorbach, M. Hallett, Identifying true brain interaction from EEG data using the imaginary part of coherency
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 115:2292-2307, 2004 [bibtex]

E. Oja, S. Harmeling, L. Almeida, Independent component analysis and beyond - Editorial for the special section on ICA
Signal Processing, 2004 [bibtex]

M. Sugiyama, M. Kawanabe, K. Müller, Trading Variance Reduction with Unbiasedness: The Regularized Subspace Information Criterion for Robust Model Selection in Kernel Regression
Neural Computation, 16(5):1077-1104, 2004 [bibtex] [pdf]

M. Sugiyama, Y. Okabe, H. Ogawa, Perturbation Analysis of a Generalization Error Estimator
Neural Information Processing - Letters and Reviews, 2(2):33-38, 2004 [bibtex] [pdf]

K. Tsuda, S. Akaho, M. Kawanabe, K. Müller, Asymptotic properties of the Fisher kernel
Neural Computation, 16:115-137, 2004 [bibtex]

A. Ziehe, P. Laskov, G. Nolte, K. Müller, A Fast Algorithm for Joint Diagonalization with Non-orthogonal Transformations and its Application to Blind Source Separation
Journal of Machine Learning Research, 5:777-800, 2004 [bibtex] [pdf]

Book chapters

S. Mika, C. Schäfer, P. Laskov, D. Tax, K. Müller, Support Vector Machines
Handbook of Computational Statistics, Springer, Berlin, 2004 [bibtex]

H. Purwins, T. Graepel, B. Blankertz, K. Obermayer, Correspondence Analysis for Visualizing Interplay of Pitch Class, Key, and Composer, ISBN 3-923486-57-X
Perspectives in Mathematical and Computational Music Theory, epOs Verlag, 2004 [bibtex] [pdf]

G. Rätsch, S. Sonnenburg, Accurate Splice Site Prediction for Caenorhabditis Elegans
Kernel Methods in Computational Biology, MIT Press, MIT Press series on Computational Molecular Biology, 2004 [bibtex] [pdf]

Conference papers

G. Blanchard, C. Schäfer, Y. Rozenholc, Oracle bounds and exact algorithm for dyadic classification trees
Proceedings of the 17th. Conference on Learning Theory (COLT 2004), Springer, 2004 [bibtex] [pdf]

O. Bousquet, L. Zwald, G. Blanchard, Statistical properties of Kernel Principal Component Analysis, to appear in Springer Lecture Notes in Artificial Intelligence
Proceedings of the 17th. Conference on Learning Theory (COLT 2004), 2004 [bibtex] [pdf]

G. Dornhege, B. Blankertz, G. Curio, K. Müller, Increase Information Transfer Rates in BCI by CSP Extension to Multi-class
Advances in Neural Information Processing Systems, MIT Press, 16:733-740, 2004 [bibtex] [pdf]

M. Kawanabe, New algorithms for blind separation when sources have spatial variance dependencies, accepted
Proc. of the symposium on Brain Inspired Cognitive Systems (BICS2004), 2004 [bibtex] [pdf]

M. Kawanabe, K. Müller, Estimating functions for blind separation when sources have variance-dependencies, accepted
Proc. of ICA2004, 2004 [bibtex] [pdf]

J. Kohlmorgen, Tracking and Visualization of Changes in High-Dimensional Non-Parametric Distributions
Machine Learning for Signal Processing XIV, IEEE, 2004 [bibtex] [pdf]

M. Krauledat, G. Dornhege, B. Blankertz, F. Losch, G. Curio, K. Müller, Improving Speed And Accuracy Of Brain-Computer Interfaces Using Readiness Potential Features
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 4:4511-4515, 2004 [bibtex] [pdf] [url]

P. Laskov, C. Schäfer, I. Kotenko, Intrusion detection in unlabeled data with quarter-sphere Support Vector Machines
Detection of Intrusions and Malware, and Vulnerability Assessment, Proc. of DIMVA Conference, 2004 [bibtex]

F. C. Meinecke, S. Harmeling, K. Müller, Robust ICA for Super-Gaussian Sources
Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2004), 2004 [bibtex] [pdf]

H. Purwins, B. Blankertz, G. Dornhege, K. Obermayer, Scale Degree Profiles from Audio Investigated with Machine Learning Techniques
Audio Engineering Society 116th Convention, 2004 [bibtex] [pdf]

C. v. Wrede, P. Laskov, Using classification to determine the number of finger strokes on a multi-touch tactile device
Proc. ESANN, 2004 [bibtex] [pdf]

A. Yeredor, A. Ziehe, K. Müller, Approximate joint diagonalization using a natural-gradient approach, Proc. ICA 2004
Lecture Notes in Computer Science, Springer-Verlag, 3195:89-96, 2004 [bibtex]

PhD theses

R. Krepki, Brain-Computer Interfaces: Design and Implementation of an Online BCI System of the Control in Gaming Applications and Virtual Limbs
Technische Universität Berlin, Fakultät IV - Elektrotechnik und Informatik, 2004 [bibtex]

2003

Journal papers

G. Blanchard, G. Lugosi, N. Vayatis, On the rate of convergence of regularized Boosting classifiers.
Journal of Machine Learning Research, 4:861-894, 2003 [bibtex] [pdf]

B. Blankertz, G. Dornhege, C. Schäfer, R. Krepki, J. Kohlmorgen, K. Müller, V. Kunzmann, F. Losch, G. Curio, Boosting Bit Rates and Error Detection for the Classification of Fast-Paced Motor Commands Based on Single-Trial EEG Analysis
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 11(2):127-131, 2003 [bibtex] [pdf] [url]

S. Harmeling, A. Ziehe, M. Kawanabe, K. Müller, Kernel-based Nonlinear Blind Source Separation
Neural Computation, 15:1089-1124, 2003 [bibtex]

K. Müller, C. W. Anderson, G. E. Birch, Linear and Non-Linear Methods for Brain-Computer Interfaces
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 11(2):165-169, 2003 [bibtex] [pdf]

S. Mika, G. Rätsch, J. Weston, B. Schölkopf, A. Smola, K. Müller, Constructing Descriptive and Discriminative Non-Linear Features: Rayleigh Coefficients in Kernel Feature Spaces
IEEE Transaction on Pattern Analysis and Machine Intelligence, 25(5):623-628, 2003 [bibtex]

S. Mika, G. Rätsch, J. Weston, B. Schölkopf, A. J. Smola, K. Müller, Learning discriminative and invariant nonlinear features
IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5):623-628, 2003 [bibtex]

V. Roth, J. Laub, M. Kawanabe, J. Buhmann, Optimal Cluster Preserving Embedding of Non-Metric Proximity Data
IEEE Trans. PAMI, 25:1540-1551, 2003 [bibtex]

P. Sajda, A. Gerson, K. Müller, B. Blankertz, L. Parra, A Data Analysis Competition to Evaluate Machine Learning Algorithms for use in Brain-Computer Interfaces
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 11(2):184-185, 2003 [bibtex] [pdf] [url]

D. Tax, P. Juszczak, Kernel whitening for one-class classification
International journal of pattern recognition and artificial intelligence, 17(3):333-348, 2003 [bibtex]

M. K. Warmuth, J. Liao, G. Rätsch, M. M., S. Putta, C. Lemmem, Active Learning with Support Vector Machines in the Drug Discovery Process
Journal of Chemical Information and Computer Sciences, 43(2):667-673, 2003 [bibtex]

A. Ziehe, M. Kawanabe, S. Harmeling, K. Müller, Blind separation of post-nonlinear mixtures using linearizing transformations and temporal decorrelation
Journal of Machine Learning Research, 4:1319-1338, 2003 [bibtex] [pdf]

Conference papers

G. Dornhege, B. Blankertz, G. Curio, Speeding up classification of multi-channel Brain-Computer Interfaces: Common spatial patterns for slow cortical potentials
Proceedings of the 1st International IEEE EMBS Conference on Neural Engineering. Capri 2003, 2003 [bibtex] [pdf]

G. Dornhege, B. Blankertz, G. Curio, K. Müller, Combining Features for BCI
Advances in Neural Inf. Proc. Systems (NIPS 02), 15:1115-1122, 2003 [bibtex] [pdf]

S. Harmeling, F. C. Meinecke, K. Müller, Analysing ICA components by injecting noise
Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2003), 2003 [bibtex] [pdf]

J. Kohlmorgen, On Optimal Segmentation of Sequential Data
Neural Networks for Signal Processing XIII, IEEE, 2003 [bibtex] [pdf]

R. Krepki, B. Blankertz, G. Curio, K. Müller, The Berlin Brain-Computer Interface (BBCI): towards a new communication channel for online control of multimedia applications and computer games
9th International Conference on Distributed Multimedia Systems (DMS'03), 2003 [bibtex] [pdf]

G. Rätsch, Robust Multi-Class Boosting
Proc. Euro-Speech, 2003 [bibtex]

G. Rätsch, A. Smola, S. Mika, Adapting Codes and Embeddings for Polychotomies, Slides of the talk at the main conference: PS,PDF
Advances in Neural information processing systems, 15, 2003 [bibtex]

G. Rätsch, A. Smola, S. Mika, Adapting Codes and Embeddings for Polychotomies
Advances in Neural Information Processing 15, MIT Press, 2003 [bibtex]

V. Roth, J. Laub, J. Buhmann, K. Müller, Going metric: Denoising pairwise data
Advances in Neural Information Processing 15, MIT Press, 2003 [bibtex]

D. Tax, P. Laskov, Online SVM learning: from classification to data description and back
Proc. NNSP, 2003 [bibtex] [pdf]

D. Tax, K. Müller, Feature extraction for one-class classification
ICANN/ICONIP, 2003 [bibtex] [pdf]

K. Tsuda, M. Kawanabe, K. Müller, Clustering with the Fisher score
Advances in Neural Information Processing 15, MIT Press, 2003 [bibtex]

O. Weiss, A. Ziehe, H. Herzel, Optimizing Property Codes in Protein Data Reveals Structural Characteristics
ICANN/ICONIP 2003, 2003 [bibtex] [pdf]

A. Ziehe, M. Kawanabe, S. Harmeling, K. Müller, Blind Separation of Post-Nonlinear Mixtures using Gaussianizing Transformations and Temporal Decorrelation
Proc. 4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), 2003 [bibtex]

A. Ziehe, P. Laskov, K. Müller, G. Nolte, A Linear Least-Squares Algorithm for Joint Diagonalization
Proc. 4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), 2003 [bibtex]

2002

Journal papers

F. C. Meinecke, A. Ziehe, M. Kawanabe, K. Müller, A Resampling Approach to Estimate the Stability of one- or multidimensional Independent Components
IEEE transactions on bio-medical engineering, 49(12):1514-1525, 2002 [bibtex] [pdf]

N. Murata, M. Kawanabe, A. Ziehe, K. Müller, S. Amari, On-line learning in changing environments with applications in supervised and unsupervised learning
Neural Networks, 15(4-6):743-760, 2002 [bibtex]

G. Rätsch, A. Demiriz, K. Bennett, Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces
Machine Learning special issue on "New Methods for Model Selection and Model Combination", 48:193-221, 2002 [bibtex] [pdf]

G. Rätsch, S. Mika, B. Schölkopf, K. Müller, Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(9):1184-1199, 2002 [bibtex] [pdf]

M. Sugiyama, K. Müller, The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces
Journal of Machine Learning Research, 3(Nov):323-359, 2002 [bibtex] [pdf]

K. Tsuda, M. Kawanabe, G. Rätsch, S. Sonnenburg, K. Müller, A New Discriminative Kernel from Probabilistic Models
Neural Computation, 14:2397-2414, 2002 [bibtex] [pdf] [ps]

K. Tsuda, M. Sugiyama, K. Müller, Subspace Information Criterion for Non-Quadratic Regularizers - Model Selection for Sparse Regressors
IEEE Transactions on Neural Networks, 13(1):70-80, 2002 [bibtex] [pdf]

K. Tsuda, M. Sugiyama, K. Müller, Subspace Information Criterion for Sparse Regressors, in Japanese
IEICE Transactions, 85-D-II(5):766-775, 2002 [bibtex] [pdf]

Conference papers

B. Blankertz, G. Curio, K. Müller, Classifying Single Trial EEG: Towards Brain Computer Interfacing
Advances in Neural Inf. Proc. Systems (NIPS 01), 14:157-164, 2002 [bibtex] [pdf]

B. Blankertz, C. Schäfer, G. Dornhege, G. Curio, Single Trial Detection of EEG Error Potentials: A Tool for Increasing BCI transmission rates
Artificial Neural Networks - ICANN 2002, 2002 [bibtex] [pdf]

S. Harmeling, A. Ziehe, M. Kawanabe, K. Müller, Kernel feature spaces and Nonlinear blind source separation
Advances in Neural Information Processing Systems, MIT Press, 14, 2002 [bibtex] [pdf]

P. Juszczak, D. Tax, R. Duin, Feature scaling in support vector data description
Advanced School for Computing and Imaging 2002 Conference, 2002 [bibtex] [pdf]

J. Kohlmorgen, B. Blankertz, A Simple Generative Model for Single-Trial EEG Classification
Artificial Neural Networks - ICANN 2002, Springer, 2002 [bibtex] [pdf]

J. Kohlmorgen, S. Lemm, A Dynamic HMM for On-line Segmentation of Sequential Data
Advances in Neural Information Processing Systems 14, MIT Press, 2002 [bibtex] [pdf]

C. Lai, D. Tax, R. Duin, E. Pekalska, P. Paclik, On combining one-class classifiers for image database retrieval
3rd international workshop on multiple classifier systems, 2002 [bibtex] [pdf]

F. C. Meinecke, A. Ziehe, M. Kawanabe, K. Müller, Estimating the Reliability of ICA Projections
Advances in Neural Information Processing Systems 14, MIT Press, 2002 [bibtex] [pdf]

E. Pekalska, D. Tax, R. Duin, One-class LP classifier for dissimilarity representations
Neural Information Processing Systems, 2002 [bibtex] [pdf]

G. Rätsch, S. Mika, M. Warmuth, On the Convergence of Leveraging, Longer version also NeuroCOLT Technical Report NC-TR-2001-098
Advances in Neural information processing systems, 14, 2002 [bibtex] [pdf]

S. Sonnenburg, G. Rätsch, A. Jagota, K. Müller, New Methods for Splice-Site Recognition, Copyright by Springer
In Proceedings of the International Conference on Artifical Neural Networks., 2002 [bibtex] [pdf] [ps]

M. Sugiyama, K. Müller, Selecting Ridge Parameters in Infinite Dimensional Hypothesis Spaces
Artificial Neural Networks, Springer, Lecture Notes in Computer Science, 2415:528-534, 2002 [bibtex] [pdf]

K. Tsuda, M. Kawanabe, The leave-one-out kernel
Artificial Neural Networks - ICANN 2002, 2002 [bibtex]

K. Tsuda, M. Kawanabe, G. Rätsch, S. Sonnenburg, K. Müller, A New Discriminative Kernel from Probabilistic Models
Advances in Neural information processings systems, 14, 2002 [bibtex] [pdf] [ps]

R. Vigário, A. Ziehe, K. Müller, J. Särelä, E. Oja, V. Jousmäki, G. Wübbeler, L. Trahms, B. Mackert, G. Curio, Blind decomposition of multimodal and DC evoked responses, ISBN 0-262-19481-3
Advances in Exploratory analysis and data modeling in functional neuroimaging, MIT Press, Cambridge MA., 2002 [bibtex]

M. Warmuth, G. Rätsch, M. Mathieson, J. Liao, C. Lemmen, Active Learning in the Drug Discovery Process
Advances in Neural Information Processings Systems, 14:1449-1456, 2002 [bibtex]

Technical reports

S. Harmeling, A. Ziehe, M. Kawanabe, K. Müller, Kernel-based Nonlinear Blind Source Separation
BLISS project, 2002 [bibtex]

V. Roth, J. Laub, M. Kawanabe, J. Buhmann, Optimal Cluster Preserving Embedding of Non-Metric Proximity Data
University of Bonn, 2002 [bibtex]

PhD theses

S. Mika, Kernel Fisher Discriminants
University of Technology, Berlin, 2002 [bibtex] [pdf]

2001

Journal papers

K. Müller, S. Mika, G. Rätsch, K. Tsuda, B. Schölkopf, An Introduction to Kernel-based Learning Algorithms
IEEE Neural Networks, 12(2):181-201, 2001 [bibtex]

N. Murata, S. Ikeda, A. Ziehe, An Approach to Blind Source Separation Based on Temporal Structure of Speech Signals
Neurocomputing, 41(1-4):1-24, 2001 [bibtex]

G. Nolte, A. Ziehe, K. Müller, Noise robust estimates of correlation dimension and K2 entropy, PACS: 02.50.-r, 05.45.Tp, 05.45.Ac
Phys.rev.E, 64(1):016112, 2001 [bibtex]

T. Onoda, G. Rätsch, K. Müller, An Arcing algorithm with an intuitive learning control parameter, in Japanese, a similar version in english appeared in Advances in Large Margin Classifiers
Journal of the Japanese Society for AI, 16(5C):417-426, 2001 [bibtex] [pdf]

G. Rätsch, T. Onoda, K. Müller, Soft Margins for AdaBoost
Machine Learning, Kluwer Academic Publishers, 42(3):287-320, 2001 [bibtex]

A. Smola, S. Mika, B. Schölkopf, R. Williamson, Regularized Principal Manifolds
Journal of Machine Learning Research, MIT Press, 1:179-209, 2001 [bibtex] [pdf]

Conference papers

S. Harmeling, A. Ziehe, M. Kawanabe, B. Blankertz, K. Müller, Nonlinear blind source separation using kernel feature spaces
Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2001), 2001 [bibtex] [pdf]

J. Kohlmorgen, S. Lemm, An On-line Method for Segmentation and Identification of Non-stationary Time Series
Neural Networks for Signal Processing XI, IEEE, 2001 [bibtex] [pdf]

F. C. Meinecke, A. Ziehe, M. Kawanabe, K. Müller, Assessing reliability of ICA projections - a resampling approach
Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2001), 2001 [bibtex] [pdf]

S. Mika, G. Rätsch, K. Müller, A mathematical programming approach to the Kernel Fisher algorithm
Advances in Neural Information Processing Systems, MIT Press, 13:591-597, 2001 [bibtex] [pdf]

S. Mika, A. Smola, B. Schölkopf, An Improved Training Algorithm for Kernel Fisher Discriminants
Proceedings AISTATS 2001, Morgan Kaufmann, 2001 [bibtex] [pdf]

H. Purwins, B. Blankertz, K. Obermayer, Constant Q Profiles for Tracking Modulations in Audio Data
International Computer Music Conference (Cuba), 2001 [bibtex] [pdf]

K. Tsuda, G. Rätsch, S. Mika, K. Müller, Learning To Predict the Leave-one-out Error of Kernel based classifiers
Proc. ICANN'01, 2001 [bibtex] [pdf]

A. Ziehe, M. Kawanabe, S. Harmeling, K. Müller, Separation of post-nonlinear mixtures using ACE and temporal decorrelation
Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2001), 2001 [bibtex]

A. Ziehe, G. Nolte, T. Sander, K. Müller, G. Curio, A comparison of ICA-based artifact reduction methods for MEG
Recent Advances in Biomagnetism, Proc. of the 12th International conference on Biomagnetism, Helsinki University of Technology, 2001 [bibtex] [pdf]

Technical reports

G. Rätsch, S. Mika, M. Warmuth, On the Convergence of Leveraging, Shorter version accepted at NIPS*01.
Royal Holloway College, 2001 [bibtex] [pdf] [ps]

G. Rätsch, M. Warmuth, Marginal Boosting
Royal Holloway College, 2001 [bibtex] [pdf] [ps]

PhD theses

G. Rätsch, Robust Boosting via Convex Optimization
University of Potsdam, 2001 [bibtex] [pdf]

2000

Books

S. Solla, T. Leen, K. (. Müller, Advances in Neural Information Processing System 12 (NIPS'99)
MIT Press, 2000 [bibtex]

Journal papers

J. Kohlmorgen, K. Müller, J. Rittweger, K. Pawelzik, Identification of Nonstationary Dynamics in Physiological Recordings, The original publication is available on LINK at http://link.springer.de.
Biological cybernetics, Springer, 83(1):73-84, 2000 [bibtex] [pdf]

S. Liehr, K. Pawelzik, J. Kohlmorgen, S. Lemm, K. Müller, Prediction of Financial Data with Hidden Markov Mixtures of Experts
Int. Journal of Theoretical and Applied Finance, World Scientific, 3(3):593, 2000 [bibtex]

T. Onoda, G. Rätsch, K. Müller, An asymptotical Analysis and Improvement of AdaBoost in the binary classification case, In japanese
Journal of the Japanese Society for AI, 15(2):287-296, 2000 [bibtex]

H. Purwins, B. Blankertz, K. Obermayer, Computing Auditory Perception
Organised Sound, 5(3):159-171, 2000 [bibtex] [pdf]

G. Wübbeler, A. Ziehe, B. Mackert, K. Müller, L. Trahms, G. Curio, Independent Component Analysis of Non-invasively Recorded Cortical Magnetic DC-fields in Humans
IEEE Transactions on Biomedical Engineering, 47(5):594-599, 2000 [bibtex]

A. Ziehe, K. Müller, G. Nolte, B. Mackert, G. Curio, Artifact reduction in magnetoneurography based on time-delayed second-order correlations
IEEE transactions on bio-medical engineering, 47(1):75-87, 2000 [bibtex]

A. Zien, G. Rätsch, S. Mika, B. Schölkopf, T. Lengauer, K. Müller, Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites in DNA
BioInformatics, 16(9):799-807, 2000 [bibtex] [pdf]

Book chapters

G. Rätsch, B. Schölkopf, A. Smola, S. Mika, T. Onoda, K. Müller, Robust Ensemble Learning, similarly appeared in the Journal of the Japanese Society of AI, 2001
Proc. of the NIPS*98 Workshop on Large Margin Classifiers: Advances in Large Margin Classifiers, MIT Press, 2000 [bibtex] [pdf]

Conference papers

J. Kohlmorgen, S. Lemm, G. Rätsch, K. Müller, Analysis of Nonstationary Time Series by Mixtures of Self-Organizing Predictors
Neural Networks for Signal Processing X, IEEE, 2000 [bibtex] [pdf]

K. Müller, J. Kohlmorgen, A. Ziehe, B. Blankertz, Decomposition Algorithms for Analysing Brain Signals
Adaptive Systems for Signal Processing, Communications and Control, 2000 [bibtex] [pdf] [url]

K. Müller, G. Rätsch, J. Kohlmorgen, A. Smola, B. Schölkpf, V. Vladimir, Time series prediction using support vector regression and neural networks
2nd International Sympsium on Frontiers of Time Series Modelling: Nonparametric Approach to Knowledge Diescovery, Instiute of mathematical statistics publication, 2000 [bibtex]

S. Mika, G. Rätsch, J. Weston, B. Schölkopf, A. Smola, K. Müller, Invariant Feature Extraction and Classification in Kernel Spaces
Proc. NIPS 12, MIT Press, 2000 [bibtex] [pdf]

T. Onoda, G. Rätsch, K. Müller, A Non-Intrusive Monitoring System for Household Electric Appliances with Inverters
Proc. of NC'2000, 2000 [bibtex] [pdf]

L. Parra, C. Spence, P. Sajda, A. Ziehe, K. Müller, Unmixing Hyperspectral Data
Advances in Neural Information Processing Systems, MIT Press, 12:942-948, 2000 [bibtex]

H. Purwins, B. Blankertz, K. Obermayer, Modelle der Musikwahrnehmung zwischen auditorischer Neurophysiologie und Psychoakustik
KlangForschung '99, Pfau-Verlag, 2000 [bibtex] [pdf]

G. Rätsch, B. Schölkopf, A. Smola, S. Mika, T. Onoda, K. Müller, Robust Ensemble Learning for Data Mining, This is a short version of iteRaeSchSmoMikOnoMue00
Proceedings of PAKDD'2000, Lecture Notes in Artificial Intelligence, Springer, 2000 [bibtex] [pdf]

G. Rätsch, B. Schölkopf, A. Smola, K. Müller, T. Onoda, S. Mika, u -Arc: Ensemble Learning in the Presence of Outliers
Proc. NIPS 12, MIT Press, 2000 [bibtex] [pdf]

G. Rätsch, M. Warmuth, S. Mika, T. Onoda, S. Lemm, K. Müller, Barrier Boosting
Proc. COLT'00, Morgan Kaufmann, 2000 [bibtex] [pdf]

A. Ziehe, G. Nolte, G. Curio, K. Müller, OFI: Optimal filtering algorithms for Source Separation
ICA 2000, 2000 [bibtex]

Technical reports

S. Mika, A. Smola, B. Schölkopf, An Improved Training Algorithm for Kernel Fisher Discriminants, see also above, AISTATS 2001
Microsoft Research, 2000 [bibtex]

T. Onoda, G. Rätsch, Trends in Boosting Research and Applications
Central Research Institute of the Electric Power Industry, 2000 [bibtex]

G. Rätsch, A. Demiriz, K. Bennett, Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces, accepted for publication in the Machine Learning journal special issue on "New Methods for Model Selection and Model Combination"
Royal Holloway College, 2000 [bibtex] [pdf]

G. Rätsch, B. Schölkopf, S. Mika, K. Müller, SVM and Boosting: One Class
GMD FIRST, 2000 [bibtex] [pdf]

1999

Journal papers

S. Liehr, K. Pawelzik, J. Kohlmorgen, K. Müller, Hidden Markov Mixtures of Experts with an Application to EEG Recordings from Sleep
Theory in Biosciences, 118(3-4):246-260, 1999 [bibtex] [pdf]

B. Schölkopf, S. Mika, C. Burges, P. Knirsch, K. Müller, G. Rätsch, A. Smola, Input Space vs. Feature Space in Kernel-Based Methods
IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council, 10(5):1000-1017, 1999 [bibtex] [pdf]

Conference papers

T. Graepel, R. Herbrich, B. Schölkopf, A. Smola, P. Bartlett, K. Müller, K. Obermayer, R. Williamson, Classification on Proximity Data with LP-Machines
Proceedings of ICANN'99, IEE Press, 1:304-309, 1999 [bibtex]

J. Kohlmorgen, S. Lemm, K. Müller, S. Liehr, K. Pawelzik, Fast Change Point Detection in Switching Dynamics using a Hidden Markov Model of Prediction Experts
Artificial Neural Networks - ICANN '99, IEE, 1999 [bibtex] [pdf]

S. Liehr, K. Pawelzik, J. Kohlmorgen, S. Lemm, K. Müller, Hidden Markov Gating for Prediction of Change Points in Switching Dynamical Systems
ESANN '99: Proc. of the European Symposium on Artificial Neural Networks, D-Facto, 1999 [bibtex] [pdf]

S. Liehr, K. Pawelzik, J. Kohlmorgen, S. Lemm, K. Müller, Hidden Markov Mixtures of Experts for Prediction of Non-Stationary Dynamics
Neural Networks for Signal Processing IX, 1999 [bibtex] [pdf]

K. Müller, P. Philips, A. Ziehe, JADETD: Combining higher-order statistics and temporal information for blind source separation (with noise)
ICA '99, 1999 [bibtex]

S. Mika, G. Rätsch, J. Weston, B. Schölkopf, K. Müller, Fisher Discriminant Analysis with Kernels
Neural Networks for Signal Processing IX, IEEE, 1999 [bibtex] [pdf]

S. Mika, B. Schölkopf, A. Smola, K. Müller, M. Scholz, G. Rätsch, Kernel PCA and De-Noising in Feature Spaces
Advances in Neural Inf. Proc. Systems (NIPS 98), MIT Press, 1999 [bibtex] [pdf]

G. Rätsch, T. Onoda, K. Müller, Regularizing AdaBoost
Proc. NIPS 11, MIT Press, 1999 [bibtex] [pdf]

A. Smola, B. Schölkopf, G. Rätsch, Linear Programs for Automatic Accuracy Control in Regression
Proc. ICANN'99, Springer, 1999 [bibtex] [pdf]

A. Smola, R. Williamson, S. Mika, B. Schölkopf, Regularized Principal Manifolds
Proceedings of EuroCOLT 99), Springer, LNAI, 1572:214-229, 1999 [bibtex]

A. Ziehe, K. Müller, G. Nolte, B. Mackert, G. Curio, Artifact removal in magneto-neurographic recordings with ICA using temporal information
Recent Advances in Biomagnetism, Proc. of the 11th international conference on Biomagnetism, Tohoku University press, 1999 [bibtex]

A. Zien, G. Rätsch, S. Mika, C. L. B. Schölkopf, A. Smola, T. Lengauer, K. Mueller, Engineering Support Vector Machine Kernel That Recognize Translation Initiation Sites in DNA
Proceedings GCB'99, 1999 [bibtex] [pdf]

1998

Books

J. Kohlmorgen, Analyse schaltender und driftender Dynamik mit neuronalen Netzen, PhD Thesis (in German)
GMD, ISBN 3-88457-346-2, GMD Research Series 22/1998, 1998 [bibtex] [pdf]

G. Orr, K. Müller, (Eds.), Neural Networks: Tricks of the Trade
Springer Heidelberg, LNCS, 1998 [bibtex]

Journal papers

J. Kohlmorgen, K. Müller, Data Set A is a Pattern Matching Problem
Neural Processing Letters, Kluwer Academic Publishers, 7(1):43-47, 1998 [bibtex] [pdf]

B. Schölkopf, A. Smola, K. Müller, Nonlinear component analysis as a kernel eigenvalue problem
Neural computation, 10(5):1299-1319, 1998 [bibtex]

Book chapters

T. Hies, A. Ziehe, U. Eysholdt, K. Müller, Independent Component Analysis (ICA) von mehrkanaligen EEG-Daten mittels temporaler Dekorrelation, ISBN 3-925218-63-7
Medizinische Physik '98, Deutsche Gesellschaft für medizinische Physik, 1998 [bibtex]

K. Müller, N. Murata, A. Ziehe, S. Amari, On-line learning in Switching and Drifting environments with application to blind source separation
Cambridge University Press, On-line learning in neural networks, 1998 [bibtex]

K. Müller, A. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen, V. Vapnik, Using Support Vector Machines for Time Series Prediction
Advances in Kernel Methods - Support Vector Learning, Proc. of the NIPS Workshop on Support Vectors, MIT Press, 1998 [bibtex] [pdf]

Conference papers

J. Kohlmorgen, K. Müller, K. Pawelzik, Analysis of Drifting Dynamics with Neural Network Hidden Markov Models
Advances in Neural Information Processing Systems 10, MIT Press, 1998 [bibtex] [pdf]

T. Lee, A. Ziehe, R. Orglmeister, T. Sejnowski, Combining Time-Delayed Decorrelation and ICA: Towards Solving the Cocktail Party Problem
Proc. ICASSP98, 2:1249-1252, 1998 [bibtex]

T. Onoda, G. Rätsch, K. Müller, An asymptotic analysis of AdaBoost in the binary classification case
Proc. ICANN'98, 1998 [bibtex] [pdf]

G. Rätsch, T. Onoda, K. Müller, An improvement of AdaBoost to avoid overfitting
Proc. ICONIP, 1998 [bibtex] [pdf]

B. Schölkopf, S. Mika, A. Smola, G. Rätsch, K. Müller, Kernel PCA Pattern Reconstruction mphvia Approximate Pre-Images
Proceedings of the 8th International Conference on Artificial Neural Networks, Springer Verlag, Perspectives in Neural Computing, 1998 [bibtex] [pdf]

A. Ziehe, K. Müller, TDSEP - an efficient algorithm for blind separation using time structure
Proc. of the 8th International Conference on Artificial Neural Networks, ICANN'98, Springer Verlag, Perspectives in Neural Computing, 1998 [bibtex]

Technical reports

G. Rätsch, T. Onoda, K. Müller, Soft Margins for AdaBoost, to appear in Machine Learning
Royal Holloway College, 1998 [bibtex] [pdf]

A. Smola, S. Mika, B. Schölkopf, Quantization Functionals and Regularized Principal Manifolds
Royal Holloway College, 1998 [bibtex] [pdf]

A. Ziehe, K. Müller, G. Nolte, B. Mackert, G. Curio, Artifact Reduction in Magnetoneurography Based on Time-Delayed Second Order Correlations
GMD - German National Research Center for Information Technology, FIRST, GMD Report No. 31, 1998 [bibtex]

1997

Journal papers

S. Amari, N. Murata, K. Müller, M. Finke, H. Yang, Asymptotic Statistical Theory of Overtraining and Cross-Validation
IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council, 8(5):985-996, 1997 [bibtex]

Book chapters

B. Schölkopf, A. Smola, K. Müller, Kernel principal component analysis
Artificial Neural Networks-ICANN'97, Springer, 1997 [bibtex]

Conference papers

J. Kohlmorgen, K. Müller, K. Pawelzik, Segmentation and Identification of Drifting Dynamical Systems
Neural Networks for Signal Processing VII, IEEE, 1997 [bibtex] [pdf]

J. Kohlmorgen, K. Müller, J. Rittweger, K. Pawelzik, Analysis of Wake/Sleep EEG with Competing Experts
Artificial Neural Networks - ICANN '97, Springer, 1997 [bibtex] [pdf]

K. Müller, A. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen, V. Vapnik, Predicting Time Series with Support Vector Machines
Artificial Neural Networks - ICANN '97, Springer, LNCS, 1327:999-1004, 1997 [bibtex] [pdf]

Technical reports

K. Pawelzik, K. Müller, J. Kohlmorgen, Divisive Strategies for Predicting Non-Autonomous and Mixed Systems
GMD, 1997 [bibtex] [pdf]

1996

Journal papers

K. Pawelzik, J. Kohlmorgen, K. Müller, Annealed Competition of Experts for a Segmentation and Classification of Switching Dynamics
Neural Computation, 8:340-356, 1996 [bibtex] [pdf]

Conference papers

J. Kohlmorgen, K. Müller, K. Pawelzik, Analysis of Drifting Dynamics with Competing Predictors
Artificial Neural Networks - ICANN '96, Springer, 1996 [bibtex] [pdf]

K. Pawelzik, K. Müller, J. Kohlmorgen, Prediction of Mixtures
Artificial Neural Networks - ICANN '96, Springer, 1996 [bibtex] [pdf]

1995

Journal papers

K. Müller, J. Kohlmorgen, K. Pawelzik, Analysis of Switching Dynamics with Competing Neural Networks
IEICE Trans. on Fundamentals of Electronics, Communications and Computer Science, E78-A(10):1306-1315, 1995 [bibtex] [pdf]

Conference papers

J. Kohlmorgen, K. Müller, K. Pawelzik, Improving Short-Term Prediction with Competing Experts
Artificial Neural Networks - ICANN '95, EC2, 2:215-220, 1995 [bibtex] [pdf]

K. Müller, J. Kohlmorgen, J. Rittweger, K. Pawelzik, Analysing Physiological Data from the Wake-Sleep State Transition with Competing Predictors
NOLTA '95: Las Vegas Symposium on Nonlinear Theory and its Applications, 1995 [bibtex] [pdf]

1994

Conference papers

J. Kohlmorgen, K. Müller, K. Pawelzik, Competing Predictors Segment and Identify Switching Dynamics
Artificial Neural Networks - ICANN '94, Springer, 1994 [bibtex] [pdf]

K. Müller, J. Kohlmorgen, K. Pawelzik, Segmentation and Identification of Switching Dynamics with Competing Neural Networks
ICONIP '94: Proc. of the Int. Conf. on Neural Information Processing, 1994 [bibtex]

K. Müller, J. Kohlmorgen, K. Pawelzik, The Use of Competing Neural Networks for Segmentation and Identification of Switching Dynamics
NOLTA '94: Kagoshima Symposium on Nonlinear Theory and its Applications, 1994 [bibtex]