Bernhard Schölkopf

Director

Empirical Inference

Office: N4.019
Max-Planck-Ring 4
72076 Tübingen
Germany

+49 7071 601 551

+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 our field, 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; take a look at our last formal Research Overview and Alumni List.

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

We have written a book about causality that was just published as an open access title at MIT Press (PDF, with Jonas Peters and Dominik Janzing).
Photographs: view of the Alps from the southern black forest, a rainbow in La Palma, a lunar eclipse in 2007, the Andromeda galaxy, the Milky Way on the Roque de los Muchachos, the North America Nebula, the constellation Orion with Barnard's loop, and finally a picture of a beautiful northern light, which I took a few years ago from the plane, on the way home from a conference in Vancouver. I always try to get a window seat when flying home from the North American west coast - it is surprizingly common to see northern lights. Looking at the night sky is a fascinating and humbling experience.
Some chapters of our book Learning with Kernels.
Review paper on kernel methods in the Annals of Statistics.
Short high-level introduction on statistical learnig theory (in German) that appeared in the 2004 Jahrbuch of the Max Planck Society.
Obituary for Alexej Chervonenkis (NIPS 2014).
I am a member of the LIGO scientific collaboration to detect gravitational waves
With the growing interest in (how to make money with) big data, machine learning has significantly gained in popularity. We have published an article in the German newspaper FAZ in January 2015, discussing some of the implications. Disclaimer: the newspaper added some text that appears above our names - this was not written or approved by us.
In March 2018, I published an article about the cybernetic revolution in the German newspaper SZ. It starts with the thesis that the current revolution is about processing (generating, converting, industrializing) information in much the same way the first two industrial revolutions dealt with processing (generating, converting, industrializing) energy. I have occasionally put forward this thesis (but I'm sure I am not the only one who thinks of it this way), for instance during a NYU symposium on the future of AI in January 2016 (here are some notes written by Max Tegmark). The article also provides recommendations on what Europe should do to keep up with the development.
A children's book
I do not engage in military research, and I believe AI/ML should not be used for aggressive military purposes. Open letter against autonomous AI weapons / open letter against a military collaboration of KAIST, with positive outcome / IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems
My department and/or members of the department (incl. myself) receive funding from a number of sources including Max Planck, the DFG, the Alexander-von-Humboldt foundation, Amazon, Google, Bosch, Facebook, the BMBF (German Ministry of Science), the EU, the ETH Zürich, and the Stanford Center on Philanthropy and Civil Society.

Machine Learning Causal Inference Artificial Intelligence Computational Photography Statistics

click here

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 of the Berlin-Brandenburg Academy of Sciences and Humanities, 2012
Royal Society Milner Award, 2014
Member of the German National Academy of Science (Leopoldina) (since 2017)
Distinguished Amazon Scholar (since 2017)
Fellow of the ACM (Association for Computing Machinery) (since 2018)
Gottfried-Wilhelm-Leibniz-Preis of the German Science Foundation (2018)
Honorarprofessor at the Technical University Berlin (computer science) and at the Eberhard-Karls University Tübingen (physics)
list of publications as of January 2015
"ISI highly cited" (added in 2010)
the h Index for Computer Science
Google Scholar page
co-editor-in-chief of JMLR
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.

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.

687 results (View BibTeX file of all listed publications)

2019

Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces

Mehrjou, A., Jitkrittum, W., Schölkopf, B., Muandet, K.

2019 (conference) Submitted

arXiv [BibTex]

2019

Mehrjou, A., Jitkrittum, W., Schölkopf, B., Muandet, K. Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces 2019 (conference) Submitted

arXiv [BibTex]

2018

Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems

Mehrjou, A., Schölkopf, B.

Workshop: Infer to Control: Probabilistic Reinforcement Learning and Structured Control at the 32nd Conference on Neural Information Processing Systems, December 2018 (conference)

PDF link (url) [BibTex]

2018

Mehrjou, A., Schölkopf, B. Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems Workshop: Infer to Control: Probabilistic Reinforcement Learning and Structured Control at the 32nd Conference on Neural Information Processing Systems, December 2018 (conference)

PDF link (url) [BibTex]

Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models

Neitz, A., Parascandolo, G., Bauer, S., Schölkopf, B.

Advances in Neural Information Processing Systems 31, pages: 9838-9848, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32th Annual Conference on Neural Information Processing Systems, December 2018 (conference)

arXiv link (url) [BibTex]

Neitz, A., Parascandolo, G., Bauer, S., Schölkopf, B. Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models Advances in Neural Information Processing Systems 31, pages: 9838-9848, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32th Annual Conference on Neural Information Processing Systems, December 2018 (conference)

arXiv link (url) [BibTex]

Efficient Encoding of Dynamical Systems through Local Approximations

Solowjow, F., Mehrjou, A., Schölkopf, B., Trimpe, S.

In Proceedings of the 57th IEEE International Conference on Decision and Control (CDC), pages: 6073 - 6079 , Miami, Fl, USA, December 2018 (inproceedings)

arXiv PDF DOI Project Page [BibTex]

Solowjow, F., Mehrjou, A., Schölkopf, B., Trimpe, S. Efficient Encoding of Dynamical Systems through Local Approximations In Proceedings of the 57th IEEE International Conference on Decision and Control (CDC), pages: 6073 - 6079 , Miami, Fl, USA, December 2018 (inproceedings)

arXiv PDF DOI Project Page [BibTex]

Informative Features for Model Comparison

Jitkrittum, W., Kanagawa, H., Sangkloy, P., Hays, J., Schölkopf, B., Gretton, A.

Advances in Neural Information Processing Systems 31, pages: 816-827, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32th Annual Conference on Neural Information Processing Systems, December 2018 (conference)