Bernhard Schölkopf
Prof. Dr.
Position: Director
Room no.: 211
Phone: +49 7071 601 551
Fax: +49 7071 601 552

My scientific interests are in the field of machine learning and inference from empirical data. In particular, I study kernel methods for extracting regularities from possibly high-dimensional data. These regularities are usually statistical ones, however, in recent years I have also become interested in methods for finding causal structures that underly statistical dependences. I have worked on a number of different applications of machine learning - in data analysis, you get "to play in everyone's backyard." Most recently, I have been trying to play in the backyard of astronomers and photographers.

I am heading the Department of Empirical Inference; to learn more about our work, take a look at the Department Overview.

Many of my papers can downloaded if you click on the tab "publications;" alternatively, from http://www.kernel-machines.org/ or from arxiv.  Some additional links:

If you'd like to contact me, please consider these two notes:

1. I recently became co-editor-in-chief of JMLR. I work for JMLR because I believe in its open access model, but it takes a lot of time. During my JMLR term, please don't convince me to do other journal or grant reviewing duties.

2. I am not very organized with my e-mail so if you want to apply for a position in my lab, please send your application only to Sekretariat-Schoelkopf@tuebingen.mpg.de. Note that we do not respond to non-personalized applications that look like they are being sent to a large number of places simultaneously.

We are always happy to receive outstanding applications for PhD positions and postdocs. In particular, we are looking for PhD students with interests in general machine learning (including kernel methods and causal inference) or computational imaging (photography, astronomy, MR).

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  • D. Foreman-Mackey, B.T. Montet, D.W. Hogg, T.D. Morton, D. Wang, B. Schölkopf (2015). A systematic search for transiting planets in the K2 data The Astrophysical Journal, State: accepted
  • R. Küffner, N. Zach, R. Norel, J. Hawe, D. Schoenfeld, L. Wang, G. Li, L. Fang, L. Mackey, O. Hardiman, M. Cudkowicz, A. Sherman, G. Ertaylan, M. Grosse-Wentrup, T. Hothorn, J. Ligtenberg, JH. Macke, T. Meyer, B. Schölkopf, L. Tran, R. Vaughan, G. Stolovitzky, ML. Leitner (2015). Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression Nature Biotechnology, 33, 51-57
Conference Papers
  • M. Khatami, T. Schmidt-Wilcke, P.C. Sundgren, A. Abbasloo, B. Schölkopf, T. Schultz (2015). BundleMAP: Anatomically Localized Features from dMRI for Detection of Disease In: 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015, State: accepted
  • P. Geiger, K. Zhang, B. Schölkopf, M. Gong, D. Janzing (2015). Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components In: Proceedings of the 32nd International Conference on Machine Learning, (Ed) F. Bach and D. Blei, 37, JMLR, 1917–1925, ICML 2015
  • M. Gong, K. Zhang, B. Schölkopf, D. Tao, P. Geiger (2015). Discovering Temporal Causal Relations from Subsampled Data In: Proceedings of the 32nd International Conference on Machine Learning, (Ed) F. Bach and D. Blei, 37, JMLR, 1898–1906, ICML 2015
  • K. Zhang, J. Zhang, B. Schölkopf (2015). Distinguishing Cause from Effect Based on Exogeneity In: Fifteenth Conference on Theoretical Aspects of Rationality and Knowledge, TARK 2015, State: accepted
  • B. Huang, K. Zhang, B. Schölkopf (2015). Identification of Time-Dependent Causal Model: A Gaussian Process Treatment In: 24th International Joint Conference on Artificial Intelligence, Machine Learning Track, IJCAI15, State: accepted
  • K. Zhang, M. Gong, B. Schölkopf (2015). Multi-Source Domain Adaptation: A Causal View In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI Press, 3150-3157, AAAI 2015
Contributions to books
  • D. Wang, D. Foreman-Mackey, DW. Hogg, B. Schölkopf (2015). Calibrating the pixel-level Kepler imaging data with a causal data-driven model Workshop: 225th American Astronomical Society Meeting 2015 , 258.08
  • D. Foreman-Mackey, DW. Hogg, B. Schölkopf, D. Wang (2015). Increasing the sensitivity of Kepler to Earth-like exoplanets Workshop: 225th American Astronomical Society Meeting 2015 , 105.01D
  • R. Chaves, C. Majenz, L. Luft, TO. Maciel, D. Janzing, B. Schölkopf, D. Gross (2015). Information-Theoretic Implications of Classical and Quantum Causal Structures 18th Conference on Quantum Information Processing (QIP 2015)
  • C. Persello, A. Boularias, M. Dalponte, T. Gobakken, E. Naesset, B. Schölkopf (2014). Cost-Sensitive Active Learning With Lookahead: Optimizing Field Surveys for Remote Sensing Data Classification IEEE Transactions on Geoscience and Remote Sensing, 52, (10), 6652 - 6664
  • S. Martens, M. Bensch, S. Halder, J. Hill, F. Nijboer, A. Ramos-Murguialday, B. Schöllkopf, N. Birbaumer, A. Gharabaghi (2014). Epidural electrocorticography for monitoring of arousal in locked-in state Frontiers in Human Neuroscience, 8, (861)
  • M. Bensch, S. Martens, S. Halder, J. Hill, F. Nijboer, A. Ramos, N. Birbaumer, M. Bodgan, B. Kotchoubey, W. Rosenstiel, B. Schölkopf, A. Gharabaghi (2014). Assessing attention and cognitive function in completely locked-in state with event-related brain potentials and epidural electrocorticography Journal of Neural Engineering, 11, (2), 026006
  • J. Zscheischler, MD. Mahecha, J. Buttlar, S. Harmeling, M. Jung, A. Rammig, JT. Randerson, B. Schölkopf, SI. Seneviratne, E. Tomelleri, S. Zaehle, M. Reichstein (2014). A few extreme events dominate global interannual variability in gross primary production Environmental Research Letters, 9, (3), 035001
  • K. Zhang, Z. Wang, J. Zhang, B. Schölkopf (2014). On estimation of functional causal models: General results and application to post-nonlinear causal model ACM Transactions on Intelligent Systems and Technologies, State: accepted
Conference Papers
  • D. Lopez-Paz, S. Sra, A. Smola, Z. Ghahramani, B. Schölkopf (2014). Randomized Nonlinear Component Analysis In: Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), (Ed) Eric P. Xing and Tony Jebara, JMLR, 1359-1367, ICML 2014
  • M. Gomez Rodriguez, K. Gummadi, B. Schölkopf (2014). Quantifying Information Overload in Social Media and its Impact on Social Contagions In: Proceedings of the Eighth International Conference on Weblogs and Social Media, (Ed) E. Adar, P. Resnick, M. De Choudhury, B. Hogan, and A. Oh, AAAI Press, 170-179, ICWSM 2014
  • H. Daneshmand, M. Gomez Rodriguez, L. Song, B. Schölkopf (2014). Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm In: Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), (Ed) Eric P. Xing and Tony Jebara, JMLR, 793-801, ICML 2014
  • G. Doran, K. Muandet, K. Zhang, B. Schölkopf (2014). A Permutation-Based Kernel Conditional Independence Test In: Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI2014), (Ed) Nevin L. Zhang and Jin Tian, AUAI Press Corvallis, Oregon, 132–141, UAI2014
  • R. Chaves, L. Luft, TO. Maciel, D. Gross, D. Janzing, B. Schölkopf (2014). Inferring latent structures via information inequalities In: Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, (Ed) NL Zhang and J Tian, AUAI Press, Corvallis, Oregon, 112-121, UAI 2014
  • K. Muandet, B. Sriperumbudur, B. Schölkopf (2014). Kernel Mean Estimation via Spectral Filtering In: Advances in Neural Information Processing Systems 27, (Ed) Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger, Curran Associates, Inc., 1–9, 28th Annual Conference on Neural Information Processing Systems (NIPS 2014)