Office: N4.019

Max-Planck-Ring 4

72076 Tübingen

Germany

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

- 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)

**Learning with Local and Global Consistency**
(112), Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, June 2003 (techreport)

**Dealing with large Diagonals in Kernel Matrices**
*Annals of the Institute of Statistical Mathematics*, 55(2):391-408, June 2003 (article)

**Implicit Wiener Series**
(114), Max Planck Institute for Biological Cybernetics, June 2003 (techreport)

**Constructing Descriptive and Discriminative Non-linear Features: Rayleigh Coefficients in Kernel Feature Spaces**
*IEEE Transactions on Pattern Analysis and Machine Intelligence*, 25(5):623-628, May 2003 (article)

**Feature selection and transduction for prediction of molecular bioactivity for drug design**
*Bioinformatics*, 19(6):764-771, April 2003 (article)

ei
Bousquet, O., Schölkopf, B.
**Statistical Learning Theory**
March 2003 (talk)

**Use of the Zero-Norm with Linear Models and Kernel Methods**
*Journal of Machine Learning Research*, 3, pages: 1439-1461, March 2003 (article)

**Study of Human Classification using Psychophysics and Machine Learning**
6, pages: 149, (Editors: H.H. Bülthoff, K.R. Gegenfurtner, H.A. Mallot, R. Ulrich, F.A. Wichmann), 6. T{\"u}binger Wahrnehmungskonferenz (TWK), Febuary 2003 (poster)

**Feature Selection for Support Vector Machines by Means of Genetic Algorithms**
In *15th IEEE International Conference on Tools with AI*, pages: 142-148, 15th IEEE International Conference on Tools with AI, 2003 (inproceedings)

**Support Vector Machines**
In *Handbook of Brain Theory and Neural Networks (2nd edition)*, pages: 1119-1125, (Editors: MA Arbib), MIT Press, Cambridge, MA, USA, 2003 (inbook)

ei
Schölkopf, B.
**Kernel methods and dimensionality reduction**
2003 (talk)

**Extension of the nu-SVM range for classification**
In *Advances in Learning Theory: Methods, Models and Applications, NATO Science Series III: Computer and Systems Sciences, Vol. 190*, 190, pages: 179-196, NATO Science Series III: Computer and Systems Sciences, (Editors: J Suykens and G Horvath and S Basu and C Micchelli and J Vandewalle), IOS Press, Amsterdam, 2003 (inbook)

**An Introduction to Support Vector Machines**
In *Recent Advances and Trends in Nonparametric Statistics
*, pages: 3-17, (Editors: MG Akritas and DN Politis), Elsevier, Amsterdam, The Netherlands, 2003 (inbook)

**Statistical Learning and Kernel Methods in Bioinformatics**
In *Artificial Intelligence and Heuristic Methods in Bioinformatics*, 183, pages: 1-21, 3, (Editors: P Frasconi und R Shamir), IOS Press, Amsterdam, The Netherlands, 2003 (inbook)

**Interactive Images**
(MSR-TR-2003-64), Microsoft Research, Cambridge, UK, 2003 (techreport)

**Semi-Supervised Learning through Principal Directions Estimation**
In *ICML Workshop, The Continuum from Labeled to Unlabeled Data in Machine Learning & Data Mining*, pages: 7, ICML Workshop: The Continuum from Labeled to Unlabeled Data in Machine Learning & Data Mining, 2003 (inproceedings)

**Statistical Learning and Kernel Methods**
In *Adaptivity and Learning—An Interdisciplinary Debate*, pages: 161-186, (Editors: R.Kühn and R Menzel and W Menzel and U Ratsch and MM Richter and I-O Stamatescu), Springer, Berlin, Heidelberg, Germany, 2003 (inbook)

**A Short Introduction to Learning with Kernels**
In *Proceedings of the Machine Learning Summer School, Lecture Notes in Artificial Intelligence, Vol. 2600*, pages: 41-64, LNAI 2600, (Editors: S Mendelson and AJ Smola), Springer, Berlin, Heidelberg, Germany, 2003 (inbook)

**Bayesian Kernel Methods**
In *Advanced Lectures on Machine Learning, Machine Learning Summer School 2002, Lecture Notes in Computer Science, Vol. 2600*, LNAI 2600, pages: 65-117, 0, (Editors: S Mendelson and AJ Smola), Springer, Berlin, Germany, 2003 (inbook)

**Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond**
pages: 644, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, December 2002, Parts of this book, including an introduction to kernel methods, can be downloaded here. (book)

**Sampling Techniques for Kernel Methods **
In *Advances in neural information processing systems 14 *, pages: 335-342, (Editors: TG Dietterich and S Becker and Z Ghahramani), MIT Press, Cambridge, MA, USA, 15th Annual Neural Information Processing Systems Conference (NIPS), September 2002 (inproceedings)

**Incorporating Invariances in Non-Linear Support Vector Machines **
In *Advances in Neural Information Processing Systems 14*, pages: 609-616, (Editors: TG Dietterich and S Becker and Z Ghahramani), MIT Press, Cambridge, MA, USA, 15th Annual Neural Information Processing Systems Conference (NIPS), September 2002 (inproceedings)

**Constructing Boosting algorithms from SVMs: an application to one-class classification.**
*IEEE Transactions on Pattern Analysis and Machine Intelligence*, 24(9):1184-1199, September 2002 (article)

**Kernel Dependency Estimation**
(98), Max Planck Institute for Biological Cybernetics, August 2002 (techreport)

**Computationally Efficient Face Detection**
(MSR-TR-2002-69), Microsoft Research, June 2002 (techreport)

**Training invariant support vector machines**
*Machine Learning*, 46(1-3):161-190, January 2002 (article)

**A compression approach to support vector model selection**
(101), Max Planck Institute for Biological Cybernetics, 2002, see more detailed JMLR version (techreport)

**Feature Selection and Transduction for Prediction of Molecular Bioactivity for Drug Design**
Max Planck Institute for Biological Cybernetics / Biowulf Technologies, 2002 (techreport)

**Support Vector Machines and Kernel Methods: The New Generation of Learning Machines**
*AI Magazine*, 23(3):31-41, 2002 (article)

**A kernel approach for learning from almost orthogonal patterns**
In *Principles of Data Mining and Knowledge Discovery, Lecture Notes in Computer Science*, 2430/2431, pages: 511-528, Lecture Notes in Computer Science, (Editors: T Elomaa and H Mannila and H Toivonen), Springer, Berlin, Germany, 13th European Conference on Machine Learning (ECML) and 6th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'2002), 2002 (inproceedings)

**Kernel Methods for Extracting Local Image Semantics**
(MSR-TR-2001-99), Microsoft Research, October 2001 (techreport)

**Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators**
*IEEE Transactions on Information Theory*, 47(6):2516-2532, September 2001 (article)

**Regularized principal manifolds**
*Journal of Machine Learning Research*, 1, pages: 179-209, June 2001 (article)

**Support vector novelty detection applied to jet engine vibration spectra**
In *Advances in Neural Information Processing Systems 13*, pages: 946-952, (Editors: TK Leen and TG Dietterich and V Tresp), MIT Press, Cambridge, MA, USA, 14th Annual Neural Information Processing Systems Conference (NIPS), April 2001 (inproceedings)

**Four-legged Walking Gait Control Using a Neuromorphic Chip Interfaced to a Support Vector Learning Algorithm**
In *Advances in Neural Information Processing Systems 13*, pages: 741-747, (Editors: TK Leen and TG Dietterich and V Tresp), MIT Press, Cambridge, MA, USA, 14th Annual Neural Information Processing Systems Conference (NIPS), April 2001 (inproceedings)

**The Kernel Trick for Distances**
In *Advances in Neural Information Processing Systems 13*, pages: 301-307, (Editors: TK Leen and TG Dietterich and V Tresp), MIT Press, Cambridge, MA, USA, 14th Annual Neural Information Processing Systems Conference (NIPS), April 2001 (inproceedings)

**An Introduction to Kernel-Based Learning Algorithms**
*IEEE Transactions on Neural Networks*, 12(2):181-201, March 2001 (article)

**Estimating the support of a high-dimensional distribution.**
*Neural Computation*, 13(7):1443-1471, March 2001 (article)

**An Improved Training Algorithm for Kernel Fisher Discriminants**
In *Proceedings AISTATS*, pages: 98-104, (Editors: T Jaakkola and T Richardson), Morgan Kaufman, San Francisco, CA, Artificial Intelligence and Statistics (AISTATS), January 2001 (inproceedings)

**Computationally Efficient Face Detection**
In *Computer Vision, ICCV 2001, vol. 2*, (73):695-700, IEEE, 8th International Conference on Computer Vision, 2001 (inproceedings)

**Use of the ell_0-norm with linear models and kernel methods**
Biowulf Technologies, 2001 (techreport)

**KDD Cup 2001 data analysis: prediction of molecular bioactivity for drug design – Binding to Thrombin**
BIOwulf, 2001 (techreport)

**A kernel approach for vector quantization with guaranteed distortion bounds**
In *Artificial Intelligence and Statistics*, pages: 129-134, (Editors: T Jaakkola and T Richardson), Morgan Kaufmann, San Francisco, CA, USA, 8th International Conference on Artificial Intelligence and Statistics (AI and STATISTICS), 2001 (inproceedings)

**Incorporating Invariances in Non-Linear Support Vector Machines**
Max Planck Institute for Biological Cybernetics / Biowulf Technologies, 2001 (techreport)

**Estimating a Kernel Fisher Discriminant in the Presence of Label Noise**
In *18th International Conference on Machine Learning*, pages: 306-313, (Editors: CE Brodley and A Pohoreckyj Danyluk), Morgan Kaufmann , San Fransisco, CA, USA, 18th International Conference on Machine Learning (ICML), 2001 (inproceedings)

**A Generalized Representer Theorem**
In *Lecture Notes in Computer Science, Vol. 2111*, (2111):416-426, LNCS, (Editors: D Helmbold and R Williamson), Springer, Berlin, Germany, Annual Conference on Computational Learning Theory (COLT/EuroCOLT), 2001 (inproceedings)

**Bound on the Leave-One-Out Error for Density Support Estimation using nu-SVMs**
University of Cambridge, 2001 (techreport)

**Support Vector Regression for Black-Box System Identification**
In *11th IEEE Workshop on Statistical Signal Processing*, pages: 341-344, IEEE Signal Processing Society, Piscataway, NY, USA, 11th IEEE Workshop on Statistical Signal Processing, 2001 (inproceedings)

**Bound on the Leave-One-Out Error for 2-Class Classification using nu-SVMs**
University of Cambridge, 2001, Updated May 2003 (literature review expanded) (techreport)

**Kernel Machine Based Learning for Multi-View Face
Detection and Pose Estimation**
In *Proceedings Computer Vision, 2001, Vol. 2*, pages: 674-679, IEEE Computer Society, 8th International Conference on Computer Vision (ICCV), 2001 (inproceedings)