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 inference from empirical data, in particular machine learning and perception ( Department of Empirical Inference). 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 regularities.

To learn more about our work, you may want to take a look at the Department Overview.

Many of the papers can downloaded if you click on the tab "publications;" the older ones usually from http://www.kernel-machines.org/. A starting point is the first chapter of our book Learning with Kernels, available online. If your interest in machine learning is a mathematical one, you might prefer our review paper in the Annals of Statistics (arXiv link). For a general audience, I wrote a short high-level introduction in German that appeared in the Jahrbuch of the Max Planck Society.


Click here for a photograph of a beautiful northern light, which I took a few years ago from the plane on the way home from NIPS.


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

1. I just 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 currently have openings for PhD students in a joint program with Cambridge. If you are interested in this, check out Zoubin's recent posting on ml-worldwide (the deadline is early December).

Independently of this, 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).

  • M.Sc. in mathematics and Lionel Cooper Memorial Prize, University of London (1992)
  • Diplom in physics (Tübingen, 1994)
  • doctorate in computer science from the Technical University Berlin (1997); thesis on Support Vector Learning (main advisor: V. Vapnik, AT&T Bell Labs) won the annual dissertation prize of the German Association for Computer Science (GI)
  • scientific member of the Max Planck Society, 2001
  • awards won by his lab
  • J. K. Aggarwal Prize of the International Association for Pattern Recognition, 2006
  • Max Planck Research Award, 2011
  • Academy Prize 2012 of the Berlin-Brandenburg Academy of Sciences and Humanities
  • Honorarprofessor at the Technical University Berlin (computer science) and at the Eberhard-Karls University Tübingen (physics)
  • "highly cited" (added in 2010)
  • co-editor-in-chief of JMLR
  • board member, ACM Books
  • member of the boards of the NIPS foundation and of the International Machine Learning Society
  • PC member (e.g., NIPS, COLT, ICML, UAI, DAGM, CVPR, Snowbird Learning Workshop) and  co-chair of various conferences (COLT'03, DAGM'04, NIPS'05, NIPS'06 and the first two kernel workshops).
  • co-founder of the Machine Learning Summer Schools
  • two-page CV: PDF.

Export search results as: [BibTex]

  • 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
  • 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, AAAI 2015, State: accepted
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 , State: submitted
  • 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), State: accepted
  • 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)
  • P. Geiger, D. Janzing, B. Schölkopf (2014). Estimating Causal Effects by Bounding Confounding In: Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence , (Ed) Nevin L. Zhang and Jin Tian, AUAI Press Corvallis, Oregon , 240-249 , UAI 2014
Contributions to books
  • M. Besserve, B. Schölkopf, N. K. Logothetis (2014). Unsupervised identification of neural events in local field potentials 44th Annual Meeting of the Society for Neuroscience (Neuroscience 2014)
  • I. Bezrukov, H. Schmidt, F. Mantlik, N. Schwenzer, C. Brendle, B. Schölkopf, BJ. Pichler (2013). MR-based Attenuation Correction Methods for Improved PET Quantification in Lesions within Bone and Susceptibility Artifact Regions Journal of Nuclear Medicine, 54, (10), 1768-1774
  • T. Schultz, L. Schlaffke, B. Schölkopf, T. Schmidt-Wilcke (2013). HiFiVE: A Hilbert Space Embedding of Fiber Variability Estimates for Uncertainty Modeling and Visualization (Ed) B Preim, P Rheingans, and H Theisel, Computer Graphics Forum, 32, (3), Blackwell Publishing, Oxford, UK, 121–130, Eurographics Conference on Visualization (EuroVis) 2013
Conference Papers
  • K. Muandet, D. Balduzzi, B. Schölkopf (2013). Domain Generalization via Invariant Feature Representation In: Proceedings of the 30th International Conference on Machine Learning, W&CP 28(1), (Ed) S Dasgupta and D McAllester, JMLR, 10-18, ICML 2013
  • M. Gomez Rodriguez, J. Leskovec, B. Schölkopf (2013). Structure and Dynamics of Information Pathways in On-line Media In: 6th ACM International Conference on Web Search and Data Mining (WSDM), (Ed) S Leonardi, A Panconesi, P Ferragina, and A Gionis, ACM, 23-32, WSDM 2013
  • K. Muandet, B. Schölkopf (2013). One-class Support Measure Machines for Group Anomaly Detection In: Proceedings 29th Conference on Uncertainty in Artificial Intelligence (UAI), (Ed) Ann Nicholson and Padhraic Smyth, AUAI Press, Corvallis, Oregon, 449-458, UAI 2013
  • TO. Zander, B. Battes, B. Schölkopf, M. Grosse-Wentrup (2013). Towards neurofeedback for improving visual attention In: Proceedings of the Fifth International Brain-Computer Interface Meeting: Defining the Future, (Ed) J.d.R. Millán, S. Gao, R. Müller-Putz, J.R. Wolpaw, and J.E. Huggins, Verlag der Technischen Universität Graz, Article ID: 086, ISBN: 978-3-85125-260-6, 5th International Brain-Computer Interface Meeting
  • M. Gomez Rodriguez, J. Leskovec, B. Schölkopf (2013). Modeling Information Propagation with Survival Theory In: Proceedings of the 30th International Conference on Machine Learning, JMLR W&CP 28 (3), (Ed) S Dasgupta and D McAllester, JMLR, 666-674, ICML 2013
  • D. Lopez-Paz, P. Hennig, B. Schölkopf (2013). The Randomized Dependence Coefficient In: Advances in Neural Information Processing Systems 26, (Ed) C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger, 1–9, 27th Annual Conference on Neural Information Processing Systems (NIPS 2013)
  • J. Mooij, D. Janzing, B. Schölkopf (2013). From Ordinary Differential Equations to Structural Causal Models: the deterministic case In: Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence, (Ed) A Nicholson and P Smyth, AUAI Press, Corvallis, Oregon, 440-448, UAI 2013
  • E. Sgouritsa, D. Janzing, J. Peters, B. Schölkopf (2013). Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders In: Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI), (Ed) A Nicholson and P Smyth, AUAI Press Corvallis, Oregon, USA, 556-565, UAI 2013
  • S. Harmeling, M. Hirsch, B. Schölkopf (2013). On a link between kernel mean maps and Fraunhofer diffraction, with an application to super-resolution beyond the diffraction limit In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 1083-1090, CVPR 2013