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Using Probabilistic Movement Primitives for Striking Movements

Gomez-Gonzalez, S., Neumann, G., Schölkopf, B., Peters, J.

16th IEEE-RAS International Conference on Humanoid Robots, pages: 502-508, Humanoids, 2016 (conference)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels

Tolstikhin, I., Sriperumbudur, B. K., Schölkopf, B.

Advances in Neural Information Processing Systems 29, pages: 1930-1938, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems (NIPS), 2016 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Experimental and causal view on information integration in autonomous agents

Geiger, P., Hofmann, K., Schölkopf, B.

Proceedings of the 6th International Workshop on Combinations of Intelligent Methods and Applications (CIMA 2016), pages: 21-28, (Editors: Hatzilygeroudis, I. and Palade, V.), 2016 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Analysis of multiparametric MRI using a semi-supervised random forest framework allows the detection of therapy response in ischemic stroke

Castaneda, S., Katiyar, P., Russo, F., Calaminus, C., Disselhorst, J. A., Ziemann, U., Kohlhofer, U., Quintanilla-Martinez, L., Poli, S., Pichler, B. J.

World Molecular Imaging Conference, 2016 (talk)

link (url) [BibTex]

link (url) [BibTex]


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Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data

Schütt, H. H., Harmeling, S., Macke, J. H., Wichmann, F. A.

Vision Research, 122, pages: 105-123, 2016 (article)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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A cognitive brain–computer interface for patients with amyotrophic lateral sclerosis

Hohmann, M., Fomina, T., Jayaram, V., Widmann, N., Förster, C., Just, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.

In Brain-Computer Interfaces: Lab Experiments to Real-World Applications, 228(Supplement C):221-239, 8, Progress in Brain Research, (Editors: Damien Coyle), Elsevier, 2016 (incollection)

DOI [BibTex]

DOI [BibTex]


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Hierarchical Relative Entropy Policy Search

Daniel, C., Neumann, G., Kroemer, O., Peters, J.

Journal of Machine Learning Research, 17(93):1-50, 2016 (article)

link (url) [BibTex]

link (url) [BibTex]


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Experiments with Hierarchical Reinforcement Learning of Multiple Grasping Policies

Osa, T., Peters, J., Neumann, G.

International Symposium on Experimental Robotics (ISER), 1, pages: 160-172, Springer Proceedings in Advanced Robotics, (Editors: Dana Kulic, Yoshihiko Nakamura, Oussama Khatib and Gentiane Venture), Springer, 2016 (conference)

DOI [BibTex]

DOI [BibTex]


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Learning to Deblur

Schuler, C. J., Hirsch, M., Harmeling, S., Schölkopf, B.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(7):1439-1451, IEEE, 2016 (article)

DOI [BibTex]

DOI [BibTex]


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Transfer Learning in Brain-Computer Interfaces

Jayaram, V., Alamgir, M., Altun, Y., Schölkopf, B., Grosse-Wentrup, M.

IEEE Computational Intelligence Magazine, 11(1):20-31, 2016 (article)

PDF DOI [BibTex]

PDF DOI [BibTex]


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MERLiN: Mixture Effect Recovery in Linear Networks

Weichwald, S., Grosse-Wentrup, M., Gretton, A.

IEEE Journal of Selected Topics in Signal Processing, 10(7):1254-1266, 2016 (article)

Arxiv Code PDF DOI [BibTex]

Arxiv Code PDF DOI [BibTex]


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Causal inference using invariant prediction: identification and confidence intervals

Peters, J., Bühlmann, P., Meinshausen, N.

Journal of the Royal Statistical Society, Series B (Statistical Methodology), 78(5):947-1012, 2016, (with discussion) (article)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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An Improved Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis

Hohmann, M. R., Fomina, T., Jayaram, V., Förster, C., Just, J., M., S., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.

Proceedings of the Sixth International BCI Meeting, pages: 44, (Editors: Müller-Putz, G. R. and Huggins, J. E. and Steyrl, D.), BCI, 2016 (conference)

DOI [BibTex]

DOI [BibTex]


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Causal discovery and inference: concepts and recent methodological advances

Spirtes, P., Zhang, K.

Applied Informatics, 3(3):1-28, 2016 (article)

DOI [BibTex]

DOI [BibTex]


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On Version Space Compression

Ben-David, S., Urner, R.

Algorithmic Learning Theory - 27th International Conference (ALT 2016), 9925, pages: 50-64, Lecture Notes in Computer Science, (Editors: Ortner, R., Simon, H. U., and Zilles, S.), 2016 (conference)

DOI [BibTex]

DOI [BibTex]


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A New Trajectory Generation Framework in Robotic Table Tennis

Koc, O., Maeda, G., Peters, J.

Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 3750-3756, IROS, 2016 (conference)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Jointly Learning Trajectory Generation and Hitting Point Prediction in Robot Table Tennis

Huang, Y., Büchler, D., Koc, O., Schölkopf, B., Peters, J.

16th IEEE-RAS International Conference on Humanoid Robots, pages: 650-655, Humanoids, 2016 (conference)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Self-regulation of brain rhythms in the precuneus: a novel BCI paradigm for patients with ALS

Fomina, T., Lohmann, G., Erb, M., Ethofer, T., Schölkopf, B., Grosse-Wentrup, M.

Journal of Neural Engineering, 13(6):066021, 2016 (article)

link (url) [BibTex]

link (url) [BibTex]


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Multiparametric Imaging of Ischemic Stroke using [89Zr]-Desferal-EPO-PET/MRI in combination with Gaussian Mixture Modeling enables unsupervised lesions identification

Castaneda, S., Katiyar, P., Russo, F., Maurer, A., Patzwaldt, K., Poli, S., Calaminus, C., Disselhorst, J. A., Ziemann, U., Pichler, B. J.

European Molecular Imaging Meeting, 2016 (poster)

link (url) [BibTex]

link (url) [BibTex]


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Influence Estimation and Maximization in Continuous-Time Diffusion Networks

Gomez-Rodriguez, M., Song, L., Du, N., Zha, H., Schölkopf, B.

ACM Transactions on Information Systems, 34(2):9:1-9:33, 2016 (article)

DOI [BibTex]

DOI [BibTex]


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Anticipative Interaction Primitives for Human-Robot Collaboration

Maeda, G., Maloo, A., Ewerton, M., Lioutikov, R., Peters, J.

AAAI Fall Symposium Series. Shared Autonomy in Research and Practice, pages: 325-330, 2016 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Domain Adaptation with Conditional Transferable Components

Gong, M., Zhang, K., Liu, T., Tao, D., Glymour, C., Schölkopf, B.

Proceedings of the 33nd International Conference on Machine Learning (ICML 2016), 48, pages: 2839-2848, JMLR Workshop and Conference Proceedings, (Editors: Balcan, M.-F. and Weinberger, K. Q.), 2016 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Learning Causal Interaction Network of Multivariate Hawkes Processes

Etesami, S., Kiyavash, N., Zhang, K., Singhal, K.

Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), 2016, poster presentation (conference)

[BibTex]

[BibTex]


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Probabilistic Approximate Least-Squares

Bartels, S., Hennig, P.

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS 2016), 51, pages: 676-684, JMLR Workshop and Conference Proceedings, (Editors: Gretton, A. and Robert, C. C. ), 2016 (conference)

Abstract
Least-squares and kernel-ridge / Gaussian process regression are among the foundational algorithms of statistics and machine learning. Famously, the worst-case cost of exact nonparametric regression grows cubically with the data-set size; but a growing number of approximations have been developed that estimate good solutions at lower cost. These algorithms typically return point estimators, without measures of uncertainty. Leveraging recent results casting elementary linear algebra operations as probabilistic inference, we propose a new approximate method for nonparametric least-squares that affords a probabilistic uncertainty estimate over the error between the approximate and exact least-squares solution (this is not the same as the posterior variance of the associated Gaussian process regressor). This allows estimating the error of the least-squares solution on a subset of the data relative to the full-data solution. The uncertainty can be used to control the computational effort invested in the approximation. Our algorithm has linear cost in the data-set size, and a simple formal form, so that it can be implemented with a few lines of code in programming languages with linear algebra functionality.

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Lifelong Learning with Weighted Majority Votes

Pentina, A., Urner, R.

Advances in Neural Information Processing Systems 29, pages: 3612-3620, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems (NIPS), 2016 (conference)

link (url) [BibTex]

link (url) [BibTex]


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The population of long-period transiting exoplanets

Foreman-Mackey, D., Morton, T. D., Hogg, D. W., Agol, E., Schölkopf, B.

The Astronomical Journal, 152(6):206, 2016 (article)

link (url) [BibTex]

link (url) [BibTex]


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Statistical source separation of rhythmic LFP patterns during sharp wave ripples in the macaque hippocampus

Ramirez-Villegas, J. F., Logothetis, N. K., Besserve, M.

47th Annual Meeting of the Society for Neuroscience (Neuroscience), 2016 (poster)

[BibTex]

[BibTex]


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Screening Rules for Convex Problems

Raj, A., Olbrich, J., Gärtner, B., Schölkopf, B., Jaggi, M.

2016 (unpublished) Submitted

[BibTex]

[BibTex]


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An overview of quantitative approaches in Gestalt perception

Jäkel, F., Singh, M., Wichmann, F. A., Herzog, M. H.

Vision Research, 126, pages: 3-8, 2016 (article)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Causal and statistical learning

Schölkopf, B., Janzing, D., Lopez-Paz, D.

Oberwolfach Reports, 13(3):1896-1899, (Editors: A. Christmann and K. Jetter and S. Smale and D.-X. Zhou), 2016 (conference)

DOI [BibTex]

DOI [BibTex]


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Bootstrat: Population Informed Bootstrapping for Rare Variant Tests

Huang, H., Peloso, G. M., Howrigan, D., Rakitsch, B., Simon-Gabriel, C. J., Goldstein, J. I., Daly, M. J., Borgwardt, K., Neale, B. M.

bioRxiv, 2016, preprint (article)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control

Rueckert, E., Camernik, J., Peters, J., Babic, J.

Nature PG: Scientific Reports, 6(Article number: 28455), 2016 (article)

DOI [BibTex]

DOI [BibTex]


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TerseSVM : A Scalable Approach for Learning Compact Models in Large-scale Classification

Babbar, R., Muandet, K., Schölkopf, B.

Proceedings of the 2016 SIAM International Conference on Data Mining, pages: 234-242, (Editors: Sanjay Chawla Venkatasubramanian and Wagner Meira), SDM, 2016 (conference)

DOI [BibTex]

DOI [BibTex]


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On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection

Zhang, K., Zhang, J., Huang, B., Schölkopf, B., Glymour, C.

Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), pages: 825-834, (Editors: Ihler, A. and Janzing, D.), AUAI Press, 2016, plenary presentation (conference)

link (url) [BibTex]

link (url) [BibTex]


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The Arrow of Time in Multivariate Time Serie

Bauer, S., Schölkopf, B., Peters, J.

Proceedings of the 33rd International Conference on Machine Learning, 48, pages: 2043-2051, JMLR Workshop and Conference Proceedings, (Editors: Balcan, M. F. and Weinberger, K. Q.), JMLR, ICML, 2016 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Active Nearest-Neighbor Learning in Metric Spaces

Kontorovich, A., Sabato, S., Urner, R.

Advances in Neural Information Processing Systems 29, pages: 856-864, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems (NIPS), 2016 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Depth Estimation Through a Generative Model of Light Field Synthesis

Sajjadi, M. S. M., Köhler, R., Schölkopf, B., Hirsch, M.

Pattern Recognition: 38th German Conference, GCPR 2016, Hannover, Germany, September 12-15, 2016, Proceedings, 9796, pages: 426-438, Lecture Notes in Computer Science, (Editors: Rosenhahn, B. and Andres, B.), Springer International Publishing, 2016 (conference)

Arxiv Project link (url) DOI [BibTex]

Arxiv Project link (url) DOI [BibTex]


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Multi-view learning on multiparametric PET/MRI quantifies intratumoral heterogeneity and determines therapy efficacy

Katiyar, P., Divine, M. R., Kohlhofer, U., Quintanilla-Martinez, L., Siegemund, M., Pfizenmaier, K., Kontermann, R., Pichler, B. J., Disselhorst, J. A.

World Molecular Imaging Conference, 2016 (talk)

link (url) [BibTex]

link (url) [BibTex]


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Hippocampal neural events predict ongoing brain-wide BOLD activity

Besserve, M., Logothetis, N. K.

47th Annual Meeting of the Society for Neuroscience (Neuroscience), 2016 (poster)

[BibTex]

[BibTex]


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BOiS—Berlin Object in Scene Database: Controlled Photographic Images for Visual Search Experiments with Quantified Contextual Priors

Mohr, J., Seyfarth, J., Lueschow, A., Weber, J. E., Wichmann, F. A., Obermayer, K.

Frontiers in Psychology, 2016 (article)

DOI [BibTex]

DOI [BibTex]


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A Kernel Test for Three-Variable Interactions with Random Processes

Rubenstein, P. K., Chwialkowski, K. P., Gretton, A.

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence (UAI), (Editors: Ihler, Alexander T. and Janzing, Dominik), 2016 (conference)

PDF Supplement Arxiv [BibTex]

PDF Supplement Arxiv [BibTex]


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Continuous Deep Q-Learning with Model-based Acceleration

Gu, S., Lillicrap, T., Sutskever, I., Levine, S.

Proceedings of the 33nd International Conference on Machine Learning (ICML 2016), 48, pages: 2829-2838, JMLR Workshop and Conference Proceedings, (Editors: Maria-Florina Balcan and Kilian Q. Weinberger), JMLR.org, 2016 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Preface to the ACM TIST Special Issue on Causal Discovery and Inference

Zhang, K., Li, J., Bareinboim, E., Schölkopf, B., Pearl, J.

ACM Transactions on Intelligent Systems and Technologies, 7(2):article no. 17, 2016 (article)

DOI [BibTex]

DOI [BibTex]


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Catching heuristics are optimal control policies

Belousov, B., Neumann, G., Rothkopf, C., Peters, J.

Advances in Neural Information Processing Systems 29, pages: 1426-1434, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems (NIPS), 2016 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Bidirektionale Interaktion zwischen Mensch und Roboter beim Bewegungslernen (BIMROB)

Kollegger, G., Ewerton, M., Peters, J., Wiemeyer, J.

11. Symposium der DVS Sportinformatik, 2016 (conference)

link (url) [BibTex]

link (url) [BibTex]