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 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

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.

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Artikel (102):

Graf ABA, Bousquet O, Rätsch G und Schölkopf B (Januar-2009) Prototype Classification: Insights from Machine Learning Neural Computation 21(1) 272-300.
Steinke F und Schölkopf B (November-2008) Kernels, Regularization and Differential Equations Pattern Recognition 41(11) 3271-3286.
Hofmann M, Steinke F, Scheel V, Charpiat G, Farquhar J, Aschoff P, Brady M, Schölkopf B und Pichler BJ (Oktober-2008) MRI-Based Attenuation Correction for PET/MRI: A Novel Approach Combining Pattern Recognition and Atlas Registration Journal of Nuclear Medicine 49(11) 1875-1883.
Ben-Hur A, Ong CS, Sonnenburg S, Schölkopf B und Rätsch G (Oktober-2008) Support Vector Machines and Kernels for Computational Biology PLoS Computational Biology 4(10) 1-10.
Jäkel F, Schölkopf B und Wichmann FA (September-2008) Similarity, Kernels, and the Triangle Inequality Journal of Mathematical Psychology 52(2) 297-303.
Hinterberger T, Widmann G, Lal TN, Hill J, Tangermann M, Rosenstiel W, Schölkopf B, Elger CE und Birbaumer N (August-2008) Voluntary brain regulation and communication with electrocorticogram signals Epilepsy and Behavior 13(2) 300-306.
Laubinger S, Zeller G, Henz SR, Sachsenberg T, Widmer CK, Naouar N, Vuylsteke M, Schölkopf B, Rätsch G und Weigel D (Juli-2008) At-TAX: A Whole Genome Tiling Array Resource for Developmental Expression Analysis and Transcript Identification in Arabidopsis thaliana Genome Biology 9(7: R112) 1-16.
Hofmann T, Schölkopf B und Smola AJ (Juni-2008) Kernel Methods in Machine Learning Annals of Statistics 36(3) 1171-1220.
Jäkel F, Schölkopf B und Wichmann FA (April-2008) Generalization and Similarity in Exemplar Models of Categorization: Insights from Machine Learning Psychonomic Bulletin and Review 15(2) 256-271.
Steinke F, Hein M, Peters J und Schölkopf B (April-2008) Manifold-valued Thin-plate Splines with Applications in Computer Graphics Computer Graphics Forum 27(2) 437-448.
Sun X, Janzing D und Schölkopf B (März-2008) Causal Reasoning by Evaluating the Complexity of Conditional Densities with Kernel Methods Neurocomputing 71(7-9) 1248-1256.
Shin HH, Tsuda K und Schölkopf B (März-2008) Protein Functional Class Prediction With a Combined Graph Expert Systems with Applications 36(2) 3284-3292.
Macke JH, Maack N, Gupta R, Denk W, Schölkopf B und Borst A (Januar-2008) Contour-propagation Algorithms for Semi-automated Reconstruction of Neural Processes Journal of Neuroscience Methods 167(2) 349-357.
Jäkel F, Schölkopf B und Wichmann FA (Dezember-2007) A Tutorial on Kernel Methods for Categorization Journal of Mathematical Psychology 51(6) 343-358.
Sonnenburg S, Braun ML, Ong CS, Bengio S, Bottou L, Holmes G, LeCun Y, Müller K-R, Pereira F, Rasmussen CE, Rätsch G, Schölkopf B, Smola A, Vincent P, Weston J und Williamson RC (Oktober-2007) The Need for Open Source Software in Machine Learning Journal of Machine Learning Research 8 2443-2466.
Waldert S, Bensch M, Bogdan M, Rosenstiel W, Schölkopf B, Lowery CL, Eswaran H und Preissl H (September-2007) Real-Time Fetal Heart Monitoring in Biomagnetic Measurements Using Adaptive Real-Time ICA IEEE Transactions on Biomedical Engineering 54(10) 1867-1874.
Clark RM, Schweikert G, Toomajian C, Ossowski S, Zeller G, Shinn P, Warthmann N, Hu TT, Fu G, Hinds DA, Chen H, Frazer KA, Huson DH, Schölkopf B, Nordborg M, Rätsch G, Ecker JR und Weigel D (Juli-2007) Common Sequence Polymorphisms Shaping Genetic Diversity in Arabidopsis thaliana Science 317(5836) 338-342.
Pfingsten T, Herrmann DJL, Schnitzler T, Feustel A und Schölkopf B (Juli-2007) Feature Selection for Trouble Shooting in Complex Assembly Lines IEEE Transactions on Automation Science and Engineering 4(3) 465-469.
Pfingsten T, Herrmann DJL, Schitzler T, Feustel A und Schölkopf B (Juli-2007) Feature Selection for Troubleshooting in Complex Assembly Lines IEEE Transactions on Automation Science and Engineering 4(3) 465-469.
Rätsch G, Sonnenburg S, Srinivasan J, Witte H, Müller K-R, Sommer R-J und Schölkopf B (Februar-2007) Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning PLoS Computational Biology 3(2: e20) 0313-0322.
Franz MO und Schölkopf B (Dezember-2006) A Unifying View of Wiener and Volterra Theory and Polynomial Kernel Regression Neural Computation 18(12) 3097-3118.
Walder C, Schölkopf B und Chapelle O (September-2006) Implicit Surface Modelling with a Globally Regularised Basis of Compact Support Computer Graphics Forum 25(3) 635-644.
Bousquet O und Schölkopf B (August-2006) Comment Statistical Science 21(3) 337-340.
Sonnenburg S, Rätsch G, Schäfer C und Schölkopf B (Juli-2006) Large Scale Multiple Kernel Learning Journal of Machine Learning Research 7 1531-1565.
Hill NJ, Lal TN, Schröder M, Hinterberger T, Wilhelm B, Nijboer F, Mochty U, Widman G, Elger CE, Schölkopf B, Kübler A und Birbaumer N (Juni-2006) Classifying EEG and ECoG Signals without Subject Training for Fast BCI Implementation: Comparison of Non-Paralysed and Completely Paralysed Subjects IEEE Transactions on Neural Systems and Rehabilitation Engineering 14(2) 183-186.
Wu M, Schölkopf B und BakIr G (April-2006) A Direct Method for Building Sparse Kernel Learning Algorithms Journal of Machine Learning Research 7 603-624.
Gretton A, Belitski A, Murayama Y, Schölkopf B und Logothetis NK (April-2006) The Effect of Artifacts on Dependence Measurement in fMRI Magnetic Resonance Imaging 24(4) 401-409.
Graf ABA, Wichmann FA, Bülthoff HH und Schölkopf B (Januar-2006) Classification of Faces in Man and Machine Neural Computation 18(1) 143-165.
Gretton A, Herbrich R, Smola A, Bousquet O und Schölkopf B (Dezember-2005) Kernel Methods for Measuring Independence Journal of Machine Learning Research 6 2075-2129.
Schröder M, Lal TN, Hinterberger T, Bogdan M, Hill J, Birbaumer N, Rosenstiel W und Schölkopf B (November-2005) Robust EEG Channel Selection across Subjects for Brain-Computer Interfaces EURASIP Journal on Applied Signal Processing 2005(19) 3103-3112.
Hein M, Bousquet O und Schölkopf B (Oktober-2005) Maximal Margin Classification for Metric Spaces Journal of Computer and System Sciences 71(3) 333-359.
Kim KI, Franz MO und Schölkopf B (September-2005) Iterative Kernel Principal Component Analysis for Image Modeling IEEE Transactions on Pattern Analysis and Machine Intelligence 27(9) 1351-1366.
Schmid M, Davison TS, Henz SR, Pape UJ, Demar M, Vingron M, Schölkopf B, Weigel D und Lohmann JU (April-2005) A gene expression map of Arabidopsis thaliana development Nature Genetics 37(5) 501-506.
Chen P-H, Lin C-J und Schölkopf B (März-2005) A tutorial on ν-support vector machines Applied Stochastic Models in Business and Industry 21(2) 111-136.
Chalimourda A, Schölkopf B und Smola AJ (März-2005) Experimentally optimal ν in support vector regression for different noise models and parameter settings Neural Networks 18(2) 205-205.
Romdhani S, Torr P, Schölkopf B und Blake A (November-2004) Efficient face detection by a cascaded support-vector machine expansion Proceedings of The Royal Society of London A 460(2501) 3283-3297.
Smola AJ und Schölkopf B (August-2004) A Tutorial on Support Vector Regression Statistics and Computing 14(3) 199-222.
Lal TN, Schröder M, Hinterberger T, Weston J, Bogdan M, Birbaumer N und Schölkopf B (Juni-2004) Support Vector Channel Selection in BCI IEEE Transactions on Biomedical Engineering 51(6) 1003-1010.
von Luxburg U, Bousquet O und Schölkopf B (April-2004) A Compression Approach to Support Vector Model Selection The Journal of Machine Learning Research 5 293-323.
Chalimourda A, Schölkopf B und Smola AJ (Januar-2004) Experimentally optimal ν in support vector regression for different noise models and parameter settings Neural Networks 17(1) 127-141.
Schölkopf B (Juli-2003) Statistical Learning Theory, Capacity and Complexity Complexity 8(4) 87-94.
Weston J, Schölkopf B, Eskin E, Leslie C und Noble WS (Juni-2003) Dealing with large Diagonals in Kernel Matrices Annals of the Institute of Statistical Mathematics 55(2) 391-408.
Mika S, Rätsch G, Weston J, Schölkopf B, Smola AJ und Müller K-R (Mai-2003) Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5) 623-628.
Weston J, Perez-Cruz F, Bousquet O, Chapelle O, Elisseeff A und Schölkopf B (April-2003) Feature selection and transduction for prediction of molecular bioactivity for drug design Bioinformatics 19(6) 764-771.
Weston J, Elisseeff A, Schölkopf B und Tipping M (März-2003) Use of the Zero-Norm with Linear Models and Kernel Methods Journal of Machine Learning Research 3 1439-1461.
Cristianini N und Schölkopf B (Oktober-2002) Support Vector Machines and Kernel Methods: The New Generation of Learning Machines AI Magazine 23(3) 31-41.
Rätsch G, Mika S, Schölkopf B und Müller K-R (September-2002) Constructing Boosting algorithms from SVMs: an application to one-class classification IEEE Transactions on Pattern Analysis and Machine Intelligence 24(9) 1184-1199.
DeCoste D und Schölkopf B (Januar-2002) Training invariant support vector machines Machine Learning 46(1-3) 161-190.
Williamson RC, Smola AJ und Schölkopf B (September-2001) Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators IEEE Transactions on Information Theory 47(6) 2516-2532.
Smola AJ, Mika S, Schölkopf B und Williamson RC (Juni-2001) Regularized principal manifolds Journal of Machine Learning Research 1 179-209.
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Last updated: Montag, 22.05.2017