Image credit: Herlinde Koelbl

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. This is usually based on statistical regularities, however, I take a particular interest in causal structures that underlie 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 (PDF publication list) can downloaded if you click on the tab "publications;" alternatively, from arxiv or from http://www.kernel-machines.org/. Some additional information:

Arxiv overview paper on causality for machine learning, recommended by Judea Pearl
Book about causality, published as an open access title at MIT Press (PDF, with Jonas Peters and Dominik Janzing).
Exoplanets we have discovered; one of them (K2-18b) recently made the news as the first habitable zone exoplanet whose atmosphere contains water. CIFAR news article.
Some of my photographs: HDR composite of the 2019 solar eclipse viewed from La Silla, including the moon lit by the earth, wide angle view; 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, taken 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, and have often seen 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 learning 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 by pointing out 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 assume 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), and in some more detail in section two of this arxiv paper. 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, the Land Baden-Wuerttemberg, the Hector foundation, the Koerber foundation, CIFAR, and the Stanford Center on Philanthropy and Civil Society.
Stellungnahme zur Eindämmung der COVID-19-Pandemie

Machine Learning Causal Inference Artificial Intelligence Computational Photography Statistics

click here

Short biosketch, Long CV: PDF, short German CV: PDF.
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 (since 2001)
awards won by his lab
J. K. Aggarwal Prize of the International Association for Pattern Recognition (2006)
Max Planck Research Award (with Sebastian Thrun, 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)
Koerber European Science Prize (2019)
BBVA Foundation Frontiers of Knowledge Award (with Isabelle Guyon and Vladimir Vapnik, 2020)
Honorarprofessor at the Technical University Berlin (computer science) and at the Eberhard-Karls University Tübingen (physics)
PDF publication list as of February 2019
"ISI highly cited" (added in 2010)
Top computer scientists
the h Index for Computer Science
Google Scholar page
co-editor-in-chief of JMLR, an early open access journal and now the flagship journal of machine learning
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

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.

755 results (View BibTeX file of all listed publications)

2020

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

Karimi*, A., von Kügelgen*, J., Schölkopf, B., Valera, I.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020, *equal contribution (conference) Accepted

arXiv [BibTex]

2020

Karimi*, A., von Kügelgen*, J., Schölkopf, B., Valera, I. Algorithmic recourse under imperfect causal knowledge: a probabilistic approach Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020, *equal contribution (conference) Accepted

arXiv [BibTex]

Learning Kernel Tests Without Data Splitting

Kübler, J., Jitkrittum, W., Schölkopf, B., Muandet, K.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

[BibTex]

Kübler, J., Jitkrittum, W., Schölkopf, B., Muandet, K. Learning Kernel Tests Without Data Splitting Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

[BibTex]

Relative gradient optimization of the Jacobian term in unsupervised deep learning

Gresele, L., Fissore, G., Javaloy, A., Schölkopf, B., Hyvarinen, A.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

[BibTex]

Gresele, L., Fissore, G., Javaloy, A., Schölkopf, B., Hyvarinen, A. Relative gradient optimization of the Jacobian term in unsupervised deep learning Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

[BibTex]

Causal analysis of Covid-19 Spread in Germany

Mastakouri, A., Schölkopf, B.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

[BibTex]

Mastakouri, A., Schölkopf, B. Causal analysis of Covid-19 Spread in Germany Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

[BibTex]

MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware

Hohmann, M. R., Konieczny, L., Hackl, M., Wirth, B., Zaman, T., Enficiaud, R., Grosse-Wentrup, M., Schölkopf, B.

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (UIST), October 2020 (conference) Accepted

arXiv DOI [BibTex]

Hohmann, M. R., Konieczny, L., Hackl, M., Wirth, B., Zaman, T., Enficiaud, R., Grosse-Wentrup, M., Schölkopf, B. MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (UIST), October 2020 (conference) Accepted

arXiv DOI [BibTex]

Bayesian Online Prediction of Change Points

Agudelo-España, D., Gomez-Gonzalez, S., Bauer, S., Schölkopf, B., Peters, J.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), 124, pages: 320-329, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

Bernhard Schölkopf

2020

2020

2019

2019

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