Contact

Prof. Dr. Bernhard Schölkopf

Adresse: Spemannstr. 38
72076 Tübingen
Raum Nummer: 211
Tel.: 07071 601 551
Fax: 07071 601 552
E-Mail: bernhard.schoelkopf

 

Bild von Schölkopf, Bernhard, Prof. Dr.

Bernhard Schölkopf

Position: Direktor  Abteilung: Schölkopf

Note: We have moved to the MPI for Metals Research and are in the process of reorienting it into an MPI for Intelligent Systems (working title). For a press release in German, click here.

 

My scientific interests are in the field of inference from empirical data, in particular machine learning and perception, and I am head of the Department of Empirical Inference. In particular, I study kernel methods for extracting regularities from high-dimensional data. These regularities are usually statistical ones, however, in recent years I have also become interested in methods for finding causal regularities.

To learn more about our work, you may want to take a look at the Department Overview from the last report to our scientific advisory board, or at short project reports from the same document:

 

Many of the papers can downloaded if you click on the tab "publications;" the older ones usually from http://www.kernel-machines.org/. A starting point is the first chapter of our book Learning with Kernels, available online. If your interest in machine learning is a mathematical one, you might prefer our review paper in the Annals of Statistics (arXiv link). For a general audience, I wrote a short high-level introduction in German that appeared in the Jahrbuch of the Max Planck Society.

Click here for a photograph of a beautiful northern light, which I took a few years ago from the plane on the way home from NIPS.

Note: I am not very organized with my e-mail; if you want to apply for a position in my lab, please send your application only to Sabrina.Rehbaum@tuebingen.mpg.de.

Bernhard Schölkopf was born in Stuttgart on 20 February, 1968. He received an M.Sc. in mathematics and the Lionel Cooper Memorial Prize from the University of London in 1992, followed in 1994 by the Diplom in physics from the Eberhard-Karls-Universität, Tübingen. Three years later, he obtained a doctorate in computer science from the Technical University Berlin. His thesis on Support Vector Learning won the annual dissertation prize of the German Association for Computer Science (GI). In 1998, he won the prize for the best scientific project at the German National Research Center for Computer Science (GMD). He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). He has taught at Humboldt University, Technical University Berlin, and Eberhard-Karls-University Tübingen. In July 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in October 2002, he was appointed Honorarprofessor for Machine Learning at the Technical University Berlin. In 2006, he received the J. K. Aggarwal Prize of the International Association for Pattern Recognition, in 2011, he got the Max Planck Research Award. The ISI lists him as a highly cited researcher. He served on the editorial boards of JMLR, IEEE PAMI, and IJCV.


He is on the boards of the NIPS foundation and of the International Machine Learning Society. Members of his department have won various awards at the major machine learning conference.
Some details:

Journal of Machine Learning Research is an online journal which he helped launch as a founding action editor in early 2000. JMLR is the flagship journal of machine learning.
International Journal of Computer Vision
, one of the two flagship journals of computer vision (with IEEE PAMI, see below)
IEEE Transactions on Pattern Analysis and Machine Intelligence

Information Science and Statistics
, a Springer series of monographs
Advances in Data Analysis and Classification

With 5-year impact factors (ISI, 2008) of 10.3 and 8.0, respectively, IJCV and PAMI are the two top journals in the general area of artificial intelligence (they are ranked three and five in all of computer science). JMLR is number four (5.9).

In addition, he has served and serves as PC member (e.g., NIPS, COLT, ICML, UAI, DAGM, CVPR, Snowbird Learning Workshop) and as (program) (co-)chair of various conferences (COLT'03, DAGM'04, NIPS'05 (click here for NIPS'05 author and reviewer information), as well as the first two kernel workshops). He acted as general chair of NIPS'06.

Präferenzen: 
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Beiträge zu Tagungsbänden (207):

Shin H, Tsuda K und Schölkopf B (Dezember-2003) Protein Functional Class Prediction with a Combined Graph, Korean Data Mining Conference, 200-219.
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Fröhlich H, Chapelle O und Schölkopf B (November-2003) Feature Selection for Support Vector Machines Using Genetic Algorithms, 15th IEEE International Conference on Tools with Artificial Intelligence 2003, IEEE Operations Center, Piscataway, NJ, USA, 142-148.
Chapelle O, Weston J und Schölkopf B (Oktober-2003) Cluster Kernels for Semi-Supervised Learning In: Advances in Neural Information Processing Systems 15, , Sixteenth Annual Conference on Neural Information Processing Systems (NIPS 2002), MIT Press, Cambridge, MA, USA, 585-592.
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Weston J, Chapelle O, Elisseeff A, Schölkopf B und Vapnik V (Oktober-2003) Kernel Dependency Estimation In: Advances in Neural Information Processing Systems 15, , Sixteenth Annual Conference on Neural Information Processing Systems (NIPS 2002), MIT Press, Cambridge, MA, USA, 873-880.
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Chapelle O, Schölkopf B und Weston J (August-2003) Semi-Supervised Learning through Principal Directions Estimation, ICML 2003 Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning & Data Mining, 1-7.
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Schölkopf B und Smola AJ (2003) A Short Introduction to Learning with Kernels In: Advanced Lectures on Machine Learning, , Machine Learning Summer School 2002, Springer, Berlin, Germany, 41-64, Series: Lecture Notes in Computer Science ; 2600.
Smola AJ und Schölkopf B (2003) Bayesian Kernel Methods In: Advanced Lectures on Machine Learning, , Machine Learning Summer School 2002, Springer, Berlin, Germany, 65-117, Series: Lecture Notes in Computer Science ; 2600.
Perez-Cruz F, Weston J, Herrmann DJL und Schölkopf B (2003) Extension of the nu-SVM range for classification In: Advances in learning theory: methods, models and applications, , NATO Advanced Study Institute on Learning Theory and Practice 2002, IOS Press, Amsterdam, 179-196, Series: NATO Science Series III: Computer and Systems Sciences ; 190.
Schölkopf B, Guyon I und Weston J (2003) Statistical Learning and Kernel Methods in Bioinformatics In: Artificial intelligence and heuristic methods in bioinformatics, , NATO Advanced Research Workshop on Artificial Intelligence and Heuristic Methods in Bioinformatics 2001, IOS Press, Amsterdam, The Netherlands, 1-21, Series: NATO Science Series III: Computer and Systems Science ; 183.
Chapelle O und Schölkopf B (September-2002) Incorporating Invariances in Non-Linear Support Vector Machines In: Advances in Neural Information Processing Systems 14, , Fifteenth Annual Neural Information Processing Systems Conference (NIPS 2001), MIT Press, Cambridge, MA, USA, 609-616.
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Achlioptas D, McSherry F und Schölkopf B (September-2002) Sampling Techniques for Kernel Methods In: Advances in neural information processing systems 14, , Fifteenth Annual Neural Information Processing Systems Conference (NIPS 2001), MIT Press, Cambridge, MA, USA, 335-342.
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Schölkopf B, Weston J, Eskin E, Leslie C und Noble WS (August-2002) A kernel approach for learning from almost orthogonal patterns In: Machine Learning: ECML 2002, , 13th European Conference on Machine Learning, Springer, Berlin, Germany, 511-528, Series: Lecture Notes in Computer Science ; 2430.
Smola AJ, Mangasarian O und Schölkopf B (2002) Sparse kernel feature analysis In: Classification, Automation, and New Media, , 24th Annual Conference of the Gesellschaft für Klassifikation 2000, Springer, Berlin, Germany, 167-178, Series: Studies in Classification, Data Analysis, and Knowledge Organization.
Gretton A, Doucet A, Herbrich R, Rayner P und Schölkopf B (August-2001) Support Vector Regression for Black-Box System Identification, 11th IEEE Workshop on Statistical Signal Processing (SSP 2001), IEEE Operations Center, Piscataway, NJ, USA, 341-344.
Schölkopf B, Herbrich R und Smola AJ (Juli-2001) A Generalized Representer Theorem In: Computational Learning Theory, , 14th Annual Conference on Computational Learning Theory (COLT 2001) and 5th European Conference on Computational Learning Theory (EuroCOLT 2001), Springer, Berlin, Germany, 416-426, Series: Lecture Notes in Computer Science ; 2111.
Romdhani S, Torr P, Schölkopf B und Blake A (Juli-2001) Computationally Efficient Face Detection, Eighth IEEE International Conference on Computer Vision (ICCV 2001), IEEE Computer Society, Los Alamitos, CA, USA, 695-700.
Lawrence ND und Schölkopf B (Juli-2001) Estimating a Kernel Fisher Discriminant in the Presence of Label Noise In: Machine Learning, , Eighteenth International Conference on Machine Learning (ICML 2001), Morgan Kaufmann, San Francisco, CA, USA, 306-313.
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Buhmann J und Schölkopf B (Juli-2001) Inference Principles and Model Selection, Dagstuhl Seminar 01301, 1-22.
Cheng Y, Fu Q, Gu L, Li SZ, Schölkopf B und Zhang H (Juli-2001) Kernel Machine Based Learning for Multi-View Face Detection and Pose Estimation, Eighth IEEE International Conference on Computer Vision (ICCV 2001), IEEE Computer Society, Los Alamitos, CA, USA, 674-679.
Still S, Schölkopf B, Hepp K und Douglas RJ (April-2001) Four-legged Walking Gait Control Using a Neuromorphic Chip Interfaced to a Support Vector Learning Algorithm In: Advances in Neural Information Processing Systems 13, , Fourteenth Annual Neural Information Processing Systems Conference (NIPS 2000), MIT Press, Cambridge, MA, USA, 741-750.
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Hayton P, Schölkopf B, Tarassenko L und Anuzis P (April-2001) Support vector novelty detection applied to jet engine vibration spectra In: Advances in Neural Information Processing Systems 13, , Fourteenth Annual Neural Information Processing Systems Conference (NIPS 2000), MIT Press, Cambridge, MA, USA, 946-952.
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Schölkopf B (April-2001) The Kernel Trick for Distances In: Advances in Neural Information Processing Systems 13, , Fourteenth Annual Neural Information Processing Systems Conference (NIPS 2000), MIT Press, Cambridge, MA, USA, 301-307.
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Tipping M und Schölkopf B (Januar-2001) A kernel approach for vector quantization with guaranteed distortion bounds, 8th International Conference on Artificial Intelligence and Statistics (AISTATS 2001), Morgan Kaufman, San Francisco, CA, USA, 129-134.
Mika S, Schölkopf B und Smola AJ (Januar-2001) An Improved Training Algorithm for Kernel Fisher discriminants, 8th International Conference on Artificial Intelligence and Statistics (AISTATS 2001), Morgan Kaufman, San Francisco, CA, USA, 98-104.
Chalimourda A, Schölkopf B und Smola AJ (Juli-2000) Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2000), IEEE, Piscataway, NJ, USA, 199-204.
Williamson RC, Smola AJ und Schölkopf B (Juli-2000) Entropy Numbers of Linear Function Classes., 13th Annual Conference on Computational Learning Theory (COLT 2000), Morgan Kaufmann, San Francisco, CA, USA, 309-319.
Smola AJ und Schölkopf B (Juli-2000) Sparse Greedy Matrix Approximation for Machine Learning, Seventeenth International Conference on Machine Learning (ICML 2000), Morgan Kaufmann, San Francisco, CA, USA, 911-918.
Mika S, Rätsch G, Weston J, Schölkopf B, Smola AJ und Müller K-R (Juni-2000) Invariant feature extraction and classification in kernel spaces In: Advances in neural information processing systems 12, , Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999), MIT Press, Cambridge, MA, USA, 526-532.
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Schölkopf B, Williamson RC, Smola AJ, Shawe-Taylor J und Platt JC (Juni-2000) Support vector method for novelty detection In: Advances in Neural Information Processing Systems 12, , Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999), MIT Press, Cambridge, MA, USA, 582-588.
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Smola AJ, Shawe-Taylor J, Schölkopf B und Williamson RC (Juni-2000) The entropy regularization information criterion In: Advances in Neural Information Processing Systems 12, , Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999), MIT Press, Cambridge, MA, USA, 342-348.
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Rätsch G, Schölkopf B, Smola AJ, Müller K-R, Onoda T und Mika S (Juni-2000) v-Arc: Ensemble Learning in the Presence of Outliers In: Advances in Neural Information Processing Systems 12, , Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999), MIT Press, Cambridge, MA, USA, 561-567.
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Rätsch G, Schölkopf B, Smola AJ, Mika S, Onoda T und Müller K-R (April-2000) Robust Ensemble Learning for Data Mining In: Knowledge Discovery and Data Mining: Current Issues and New Applications, , Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2000), Springer, Berlin, Germany, 341-344, Series: Lecture Notes in Artificial Intelligence ; 1805.
Smola AJ, Bartlett PJ, Schölkopf B und Schuurmans D (2000) Advances in Large Margin Classifiers, NIPS 1998 Workshop “Advances in Large Margin Classifiers”, MIT Press, Cambridge, MA, USA, 422, Series: Neural Information Processing.
Smola AJ, Elisseeff A, Schölkopf B und Williamson RC (2000) Entropy numbers for convex combinations and MLPs In: Advances in Large Margin Classifiers, , NIPS 1998 Workshop “Advances in Large Margin Classifiers”, MIT Press, Cambridge, MA, USA, 369-387, Series: Neural Information Processing Series.
Oliver N, Schölkopf B und Smola AJ (2000) Natural Regularization from Generative Models In: Advances in Large Margin Classifiers, , NIPS 1998 Workshop “Advances in Large Margin Classifiers”, MIT Press, Cambridge, MA, USA, 51-60, Series: Neural Information Processing Series.
Rätsch G, Schölkopf B, Smola AJ, Mika S, Onoda T und Müller K-R (2000) Robust ensemble learning In: Advances in Large Margin Classifiers, , NIPS 1998 Workshop “Advances in Large Margin Classifiers”, MIT Press, Cambridge, MA, USA, 207-220, Series: Neural Information Processing Series.
Graepel T, Herbrich R, Schölkopf B, Smola AJ, Bartlett P, Müller K, Obermayer K und Williamson RC (September-1999) Classification on proximity data with LP-machines, Ninth International Conference on Artificial Neural Networks (ICANN 99), Institute of Electrical Engineers, London, UK, 304-309, Series: Conference Publication of the Institution of Electrical Engineers ; 470.
Schölkopf B, Shawe-Taylor J, Smola AJ und Williamson RC (September-1999) Kernel-dependent support vector error bounds, Ninth International Conference on Artificial Neural Networks (ICANN 99), Institute of Electrical Engineers, London, UK, 103-108, Series: Conference Publication of the Institution of Electrical Engineers ; 470.
Smola AJ, Schölkopf B und Rätsch G (September-1999) Linear programs for automatic accuracy control in regression, Ninth International Conference on Artificial Neural Networks (ICANN 99), Institute of Electrical Engineers, London, UK, 575-580, Series: Conference Publication of the Institution of Electrical Engineers ; 470.
Mika S, Rätsch G, Weston J, Schölkopf B und Müller K-R (August-1999) Fisher discriminant analysis with kernels In: Neural networks for signal processing IX, , 1999 IEEE Signal Processing Society Workshop, IEEE, Piscataway, NJ, USA, 41-48.
Mika S, Schölkopf B, Smola AJ, Müller K-R, Scholz M und Rätsch G (Juni-1999) Kernel PCA and De-noising in feature spaces In: Advances in Neural Information Processing Systems 11, , Twelfth Annual Conference on Neural Information Processing Systems (NIPS 1998), MIT Press, Cambridge, MA, USA, 536-542.
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Smola AJ, Friess T und Schölkopf B (Juni-1999) Semiparametric support vector and linear programming machines In: Advances in Neural Information Processing Systems 11, , Twelfth Annual Conference on Neural Information Processing Systems (NIPS 1998), MIT Press, Cambridge, MA, USA, 585-591.
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Schölkopf B, Bartlett PL, Smola AJ und Williamson R (Juni-1999) Shrinking the tube: a new support vector regression algorithm In: Advances in Neural Information Processing Systems 11, , Twelfth Annual Conference on Neural Information Processing Systems (NIPS 1998), MIT Press, Cambridge, MA, USA, 330-336.
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Vannerem P, Müller K-R, Smola AJ, Schölkopf B und Söldner-Rembold S (April-1999) Classifying LEP data with support vector algorithms, Conference on Artificial Intelligence in High Energy Nuclear Physics (AIHENP '99), 1-7.
Williamson RC, Smola AJ und Schölkopf B (März-1999) Entropy numbers, operators and support vector kernels In: Computational Learning Theory, , 4th European Conference on Computational Learning Theory (EuroCOLT’99), Springer, Berlin, Germany, 285-299, Series: Lecture Notes in Artificial Intelligence ; 1572.
Smola AJ, Williamson RC, Mika S und Schölkopf B (März-1999) Regularized principal manifolds In: Computational Learning Theory, , 4th European Conference on Computational Learning Theory (EuroCOLT’99), Springer, Berlin, Germany, 214-229, Series: Lecture Notes in Artificial Intelligence ; 1572.
Williamson RC, Smola AJ und Schölkopf B (1999) Entropy numbers, operators and support vector kernels. In: Advances in kernel methods: support vector learning, , Eleventh Annual Conference on Neural Information Processing (NIPS 1997), MIT Press, Cambridge, MA, 127-144.
Schölkopf B, Burges CJC und Smola AJ (1999) Introduction to support vector learning In: Advances in kernel methods: support vector learning, , Eleventh Annual Conference on Neural Information Processing (NIPS 1997), MIT Press, Cambridge, MA, USA, 1-15.
Schölkopf B, Smola AJ und Müller K-R (1999) Kernel principal component analysis In: Advances in kernel methods: support vector learning, , Eleventh Annual Conference on Neural Information Processing (NIPS 1997), MIT Press, Cambridge, MA, USA, 327-352.
Schölkopf B, Burges CJC und Smola AJ (1999) Roadmap In: Advances in kernel methods: support vector learning, , Eleventh Annual Conference on Neural Information Processing (NIPS 1997), MIT Press, Cambridge, MA, USA, 17-22.
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Last updated: Montag, 22.05.2017