Bernhard Schölkopf Computer Scientist
Bernhard Schölkopf’s scientific interests are in the fields of machine learning and inference from empirical data. In particular, he studies kernel methods for extracting regularities from possibly high-dimensional data. These regularities are usually statistical ones, however, in recent years he has also become interested in methods for finding causal structures that underlie statistical dependences. He has worked on a number of different applications of machine learning; most recently, for the work of astronomers and photographers.
Royal Society Milner Award, 2014.
Academy Prize of the Berlin-Brandenburg Academy of Sciences and Humanities, 2012.
Max Planck Research Award
J. K. Aggarwal Prize of the International Association for Pattern Recognition, 2006.
B. Schölkopf et al, "Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations," Stat. Comput., vol. 25, no. 4, pp. 755-766, July 2015.
B. Schölkopf, "Learning to see and act," Nature, vol. 518, no. 7540, pp. 486-487, Feb. 2015.
B. Schölkopf et al, "Removing systematic errors for exoplanet search via latent causes," Proceedings of The 32nd International Conference on Machine Learning, vol. 37, pp. 2218–2226, ICML 2015, 2015.
B. Schölkopf and A. Smola, "Learning with Kernels," Cambridge, MA: MIT Press, 2002.
Advisory Committee Chair Learning in Machines & Brains
Max Planck Institute for Intelligent SystemsDepartment of Empirical Inference
PhD (Computer Sciences) Technische Universität Berlin
Diplom (Physics) University of Tübingen
M.Sc. (Mathematics) University of London
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