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 links:

- 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*, discussing some of the implications.*Disclaimer: the text that appears above our names was neither written nor approved by us.* - A children's book
- open letter against autonomous AI weapons

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)
- Fellow of the ACM (Association for Computing Machinery)
- 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**.

648 results
(BibTeX)

**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)

**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)

**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)

**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)

**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)

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

**Inference Principles and Model Selection**
(01301), Dagstuhl Seminar, 2001 (techreport)

**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)

**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)

Bartlett, P., Schölkopf, B.
**Some kernels for structured data**
Biowulf Technologies, 2001 (techreport)

**Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites**
*Bioinformatics*, 16(9):799-807, September 2000 (article)

**Statistical Learning and Kernel Methods**
In *CISM Courses and Lectures, International Centre for Mechanical Sciences Vol.431*, *CISM Courses and Lectures, International Centre for
Mechanical Sciences*, 431(23):3-24, (Editors: G Della Riccia and H-J Lenz and R Kruse), Springer, Vienna, Data Fusion and Perception, 2000 (inbook)

**New Support Vector Algorithms**
*Neural Computation*, 12(5):1207-1245, May 2000 (article)

**Support vector method for novelty detection**
In *Advances in Neural Information Processing Systems 12*, pages: 582-588, (Editors: SA Solla and TK Leen and K-R Müller), MIT Press, Cambridge, MA, USA, 13th Annual Neural Information Processing Systems Conference (NIPS), June 2000 (inproceedings)

**Robust ensemble learning**
In *Advances in Large Margin Classifiers*, pages: 207-220, Neural Information Processing Series, (Editors: AJ Smola and PJ Bartlett and B Schölkopf and D. Schuurmans), MIT Press, Cambridge, MA, USA, October 2000 (inbook)

**Choosing nu in support vector regression with
different noise models — theory and experiments**
In *Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, IJCNN 2000, Neural Computing: New Challenges and Perspectives for the New Millennium*, IEEE, International Joint Conference on Neural Networks, 2000 (inproceedings)

**v-Arc: Ensemble Learning in the Presence of Outliers**
In *Advances in Neural Information Processing Systems 12*, pages: 561-567, (Editors: SA Solla and TK Leen and K-R Müller), MIT Press, Cambridge, MA, USA, 13th Annual Neural Information Processing Systems Conference (NIPS), June 2000 (inproceedings)

**An Introduction to Kernel-Based Learning Algorithms**
In *Handbook of Neural Network Signal Processing*, 4, (Editors: Yu Hen Hu and Jang-Neng Hwang), CRC Press, 2000 (inbook)

**Entropy numbers for convex combinations and MLPs**
In *Advances in Large Margin Classifiers*, pages: 369-387, Neural Information Processing Series, (Editors: AJ Smola and PL Bartlett and B Schölkopf and D Schuurmans), MIT Press, Cambridge, MA,, October 2000 (inbook)

**Invariant feature extraction and classification in kernel spaces**
In *Advances in neural information processing systems 12*, pages: 526-532, (Editors: SA Solla and TK Leen and K-R Müller), MIT Press, Cambridge, MA, USA, 13th Annual Neural Information Processing Systems Conference (NIPS), June 2000 (inproceedings)

**Robust Ensemble Learning for Data Mining**
In *Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining*, 1805, pages: 341-341, Lecture Notes in Artificial Intelligence, (Editors: H. Terano), Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2000 (inproceedings)

**Sparse greedy matrix approximation for machine learning.**
In *17th International Conference on Machine Learning, Stanford, 2000*, pages: 911-918, (Editors: P Langley), Morgan Kaufman, San Fransisco, CA, USA, 17th International Conference on Machine Learning (ICML), 2000 (inproceedings)

**Natural Regularization from Generative Models**
In *Advances in Large Margin Classifiers*, pages: 51-60, Neural Information Processing Series, (Editors: AJ Smola and PJ Bartlett and B Schölkopf and D Schuurmans), MIT Press, Cambridge, MA, USA, October 2000 (inbook)

**The Kernel Trick for Distances**
(MSR-TR-2000-51), Microsoft Research, Redmond, WA, USA, 2000 (techreport)

**Advances in Large Margin Classifiers**
pages: 422, Neural Information Processing, MIT Press, Cambridge, MA, USA, October 2000 (book)

**The entropy regularization information criterion**
In *Advances in Neural Information Processing Systems 12*, pages: 342-348, (Editors: SA Solla and TK Leen and K-R Müller), MIT Press, Cambridge, MA, USA, 13th Annual Neural Information Processing Systems Conference (NIPS), June 2000 (inproceedings)

**Entropy Numbers of Linear Function Classes.**
In *13th Annual Conference on Computational Learning Theory*, pages: 309-319, (Editors: N Cesa-Bianchi and S Goldman), Morgan Kaufman, San Fransisco, CA, USA, 13th Annual Conference on Computational Learning Theory (COLT), 2000 (inproceedings)

**Kernel method for percentile feature extraction**
(MSR-TR-2000-22), Microsoft Research, 2000 (techreport)

**Kernel principal component analysis.**
In *Advances in Kernel Methods—Support Vector Learning*, pages: 327-352, (Editors: B Schölkopf and CJC Burges and AJ Smola), MIT Press, Cambridge, MA, 1999 (inbook)

**Estimating the support of a high-dimensional distribution**
(MSR-TR-99-87), Microsoft Research, 1999 (techreport)

**Single-class Support Vector Machines**
*Dagstuhl-Seminar on Unsupervised Learning*, pages: 19-20, (Editors: J. Buhmann, W. Maass, H. Ritter and N. Tishby), 1999 (poster)

**Classifying LEP data with support vector algorithms.**
In *Artificial Intelligence in High Energy Nuclear Physics 99*, Artificial Intelligence in High Energy Nuclear Physics 99, 1999 (inproceedings)

**Generalization Bounds via Eigenvalues of the Gram matrix**
(99-035), NeuroCOLT, 1999 (techreport)

**Classification on proximity data with LP-machines**
In *Artificial Neural Networks, 1999. ICANN 99*, 470, pages: 304-309, Conference Publications , IEEE, 9th International Conference on Artificial Neural Networks, 1999 (inproceedings)

**Kernel-dependent support vector error bounds**
In *Artificial Neural Networks, 1999. ICANN 99*, 470, pages: 103-108 , Conference Publications , IEEE, 9th International Conference on Artificial Neural Networks, 1999 (inproceedings)

**Linear programs for automatic accuracy control in regression**
In *Artificial Neural Networks, 1999. ICANN 99*, 470, pages: 575-580 , Conference Publications , IEEE, 9th International Conference on Artificial Neural Networks, 1999 (inproceedings)

**Shrinking the tube: a new support vector regression algorithm**
In *Advances in Neural Information Processing Systems 11*, pages: 330-336 , (Editors: MS Kearns and SA Solla and DA Cohn), MIT Press, Cambridge, MA, USA, 12th Annual Conference on Neural Information Processing Systems (NIPS), June 1999 (inproceedings)

**Regularized principal manifolds.**
In *Lecture Notes in Artificial Intelligence, Vol. 1572*, 1572, pages: 214-229 , Lecture Notes in Artificial Intelligence, (Editors: P Fischer and H-U Simon), Springer, Berlin, Germany, Computational Learning Theory: 4th European Conference, 1999 (inproceedings)

**Entropy numbers, operators and support vector kernels.**
In *Lecture Notes in Artificial Intelligence, Vol. 1572*, 1572, pages: 285-299, Lecture Notes in Artificial Intelligence, (Editors: P Fischer and H-U Simon), Springer, Berlin, Germany, Computational Learning Theory: 4th European Conference, 1999 (inproceedings)

**Sparse kernel feature analysis**
(99-04), Data Mining Institute, 1999, 24th Annual Conference of Gesellschaft f{\"u}r Klassifikation, University of Passau (techreport)

**Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites in DNA**
In *German Conference on Bioinformatics (GCB 1999)*, October 1999 (inproceedings)

**Semiparametric support vector and linear programming machines**
In *Advances in Neural Information Processing Systems 11*, pages: 585-591 , (Editors: MS Kearns and SA Solla and DA Cohn), MIT Press, Cambridge, MA, USA, Twelfth Annual Conference on Neural Information Processing Systems (NIPS), June 1999 (inproceedings)

**Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten**
*Informatik - Forschung und Entwicklung*, 14(3):154-163, September 1999 (article)

**Kernel PCA and De-noising in feature spaces**
In *Advances in Neural Information Processing Systems 11*, pages: 536-542 , (Editors: MS Kearns and SA Solla and DA Cohn), MIT Press, Cambridge, MA, USA, 12th Annual Conference on Neural Information Processing Systems (NIPS), June 1999 (inproceedings)

**Input space versus feature space in kernel-based methods **
*IEEE Transactions On Neural Networks*, 10(5):1000-1017, September 1999 (article)

**Advances in Kernel Methods - Support Vector Learning**
MIT Press, Cambridge, MA, 1999 (book)

**Fisher discriminant analysis with kernels**
In *Proceedings of the 1999 IEEE Signal Processing Society Workshop*, 9, pages: 41-48, (Editors: Y-H Hu and J Larsen and E Wilson and S Douglas), IEEE, Neural Networks for Signal Processing IX, 1999 (inproceedings)