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. 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 - if you work 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.  Below, some additional links you may find interesting:

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).

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
  • 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: submitted
  • 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
  • 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)
  • 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