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Bernhard Schölkopf

April 24, 2023

Bernhard Schölkopf is a German computer scientist (born 20 February 1968) known for his work in machine learning, especially on kernel methods and causality. He is a director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, where he heads the Department of Empirical Inference. He is also an affiliated professor at ETH Zürich, honorary professor at the University of Tübingen and the Technical University Berlin, and chairman of the European Laboratory for Learning and Intelligent Systems (ELLIS).

Bernhard Schölkopf
BornFebruary 1968 (age 55)
Alma mater
Known for
AwardsBBVA Foundation Frontiers of Knowledge Awards (2020)
Körber European Science Prize (2019)
Causality in Statistics Education Award, American Statistical Association[1]
Leibniz Prize (2018)
Fellow of the ACM (Association for Computing Machinery) (2018)
Member of the German National Academy of Science (Leopoldina) (2017)
Milner Award (2014)
Academy Prize of the Berlin-Brandenburg Academy of Sciences and Humanities (2012)
Max Planck Research Award (2011)
J. K. Aggarwal Prize of the International Association for Pattern Recognition (2006)
Scientific career
InstitutionsMax Planck Institute for Intelligent Systems

Research

Kernel methods

Schölkopf developed SVM methods achieving world record performance on the MNIST pattern recognition benchmark at the time.[2] With the introduction of kernel PCA, Schölkopf and coauthors argued that SVMs are a special case of a much larger class of methods, and all algorithms that can be expressed in terms of dot products can be generalized to a nonlinear setting by means of what is known as reproducing kernels.[3][4] Another significant observation was that the data on which the kernel is defined need not be vectorial, as long as the kernel Gram matrix is positive definite.[5] Both insights together led to the foundation of the field of kernel methods, encompassing SVMs and many other algorithms. Kernel methods are now textbook knowledge and one of the major machine learning paradigms in research and applications.

Developing kernel PCA, Schölkopf extended it to extract invariant features and to design invariant kernels [6][7][8] and showed how to view other major dimensionality reduction methods such as LLE and Isomap as special cases. In further work with Alex Smola and others, he extended the SVM method to regression and classification with pre-specified sparsity[9] and quantile/support estimation.[10] He proved a representer theorem implying that SVMs, kernel PCA, and most other kernel algorithms, regularized by a norm in a reproducing kernel Hilbert space, have solutions taking the form of kernel expansions on the training data, thus reducing an infinite dimensional optimization problem to a finite dimensional one. He co-developed kernel embeddings of distributions methods to represent probability distributions in Hilbert Spaces,[11][12][13][14] with links to Fraunhofer diffraction[15] as well as applications to independence testing.[16][17][18]

Causality

Starting in 2005, Schölkopf turned his attention to causal inference. Causal mechanisms in the world give rise to statistical dependencies as epiphenomena, but only the latter are exploited by popular machine learning algorithms. Knowledge about causal structures and mechanisms is useful by letting us predict not only future data coming from the same source, but also the effect of interventions in a system, and by facilitating transfer of detected regularities to new situations.[19]

Schölkopf and co-workers addressed (and in certain settings solved) the problem of causal discovery for the two-variable setting[20][21][22][23][24] and connected causality to Kolmogorov complexity.[25]

Around 2010, Schölkopf began to explore how to use causality for machine learning, exploiting assumptions of independence of mechanisms and invariance.[26] His early work on causal learning was exposed to a wider machine learning audience during his Posner lecture [27] at NeurIPS 2011, as well as in a keynote talk at ICML 2017.[28] He assayed how to exploit underlying causal structures in order to make machine learning methods more robust with respect to distribution shifts[29][30][31] and systematic errors,[32] the latter leading to the discovery of a number of new exoplanets[33] including K2-18b, which was subsequently found to contain water vapour in its atmosphere, a first for an exoplanet in the habitable zone.

Education and employment

Schölkopf studied mathematics, physics, and philosophy in Tübingen and London. He was supported by the Studienstiftung and won the Lionel Cooper Memorial Prize for the best M.Sc. in Mathematics at the University of London.[34] He completed a Diplom in Physics, and then moved to Bell Labs in New Jersey, where he worked with Vladimir Vapnik, who became co-adviser of his PhD thesis at the TU Berlin (with Stefan Jähnichen). His thesis, defended in 1997, won the annual award of the German Informatics Association.[35] In 2001, following positions in Berlin, Cambridge and New York, he founded the Department for Empirical Inference at the Max Planck Institute for Biological Cybernetics, which grew into a leading center for research in machine learning. In 2011, he became founding director at the Max Planck Institute for Intelligent Systems.[36][37]

With Alex Smola, Schölkopf co-founded the series of Machine Learning Summer Schools.[38] He also co-founded a Cambridge-Tübingen PhD Programme[39] and the Max Planck-ETH Center for Learning Systems.[40] In 2016, he co-founded the Cyber Valley research consortium.[41] He participated in the IEEE Global Initiative on "Ethically Aligned Design".[42]

Schölkopf is co-editor-in-Chief of the Journal of Machine Learning Research, a journal he helped found, being part of a mass resignation of the editorial board of Machine Learning (journal). He is among the world’s most cited computer scientists.[43] Alumni of his lab include Ulrike von Luxburg, Carl Rasmussen, Matthias Hein, Arthur Gretton, Gunnar Rätsch, Matthias Bethge, Stefanie Jegelka, Jason Weston, Olivier Bousquet, Olivier Chapelle, Joaquin Quinonero-Candela, and Sebastian Nowozin.[44]

Awards

Schölkopf’s awards include the Royal Society Milner Award and, shared with Isabelle Guyon and Vladimir Vapnik, the BBVA Foundation Frontiers of Knowledge Award in the Information and Communication Technologies category. He was the first scientist working in Europe to receive this award.[45]

References

  1. ^ "Causality in Statistics Education Award". www.amstat.org.
  2. ^ Decoste, Dennis; Schölkopf, Bernhard (1 January 2002). "Training Invariant Support Vector Machines". Machine Learning. 46 (1): 161–190. doi:10.1023/A:1012454411458. hdl:11858/00-001M-0000-0013-E06A-A. S2CID 85843 – via Springer Link.
  3. ^ https://direct.mit.edu/neco/article/10/5/1299/6193/Nonlinear-Component-Analysis-as-a-Kernel, https://ei.is.tuebingen.mpg.de/publications/5634
  4. ^ Burges, Christopher J.C. (1 June 1998). "A Tutorial on Support Vector Machines for Pattern Recognition". Data Mining and Knowledge Discovery. 2 (2): 121–167. doi:10.1023/A:1009715923555. S2CID 221627509 – via Springer Link.
  5. ^ B. Schölkopf, Support Vector Learning. PhD Thesis, 1997, https://ei.is.tuebingen.mpg.de/publications/5634
  6. ^ B. Schölkopf, A. J. Smola, and K.-R. Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5):1299–1319, 1998e
  7. ^ Schölkopf, P. Simard, A. J. Smola, and V. Vapnik. Prior knowledge in support vector kernels. In M. Jordan, M. Kearns, and S. Solla, editors, Advances in Neural Information Processing Systems 10, pages 640–646, Cambridge, MA, USA, 1998d. MIT Press
  8. ^ Chapelle and B. Schölkopf. Incorporating invariances in nonlinear SVMs. In T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, pages 609–616, Cambridge, MA, USA, 2002. MIT Press
  9. ^ B. Schölkopf, A. J. Smola, R. C. Williamson, and P. L. Bartlett. New support vector algorithms. Neural Computation, 12(5):1207–1245, 2000a
  10. ^ B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson. Estimating the support of a high-dimensional distribution. Neural Computation, 13(7):1443–1471, 2001b
  11. ^ A. Gretton, K. Borgwardt, M. Rasch, B. Schölkopf and A. Smola. A Kernel Method for the Two-Sample-Problem. Advances in Neural Information Processing Systems 19: 513--520, 2007
  12. ^ A. J. Smola and A. Gretton and L. Song and B. Schölkopf. A Hilbert Space Embedding for Distributions. Algorithmic Learning Theory: 18th International Conference: 13--31, 2007
  13. ^ B. Sriperumbudur, A. Gretton, K. Fukumizu, B. Schölkopf and G. Lanckriet. Hilbert Space Embeddings and Metrics on Probability Measures. Journal of Machine Learning Research, 11: 1517--1561, 2010
  14. ^ A. Gretton, K. Borgwardt, M. Rasch, B. Schölkopf and A. J. Smola. A Kernel Two-Sample Test. Journal of Machine Learning Research, 13: 723--773, 2012
  15. ^ S. Harmeling, M. Hirsch, and B. Schölkopf. On a link between kernel mean maps and Fraunhofer diffraction, with an application to super-resolution beyond the diffraction limit. In Computer Vision and Pattern Recognition (CVPR), pages 1083–1090. IEEE, 2013
  16. ^ A. Gretton, R. Herbrich, A. J. Smola, O. Bousquet, and B. Schölkopf. Kernel methods for measuring independence. Journal of Machine Learning Research, 6:2075–2129, 2005a
  17. ^ A. Gretton, O. Bousquet, A. J. Smola and B. Schölkopf. Measuring Statistical Dependence with Hilbert-Schmidt Norms. Algorithmic Learning Theory: 16th International Conference, 2005b
  18. ^ A. Gretton, K. Fukumizu, C.H. Teo, L. Song, B. Schölkopf and A. J. Smola. A Kernel Statistical Test of Independence. Advances in Neural Information Processing Systems 20, 2007
  19. ^ B. Schölkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang, and J. Mooij. On causal and anticausal learning. In J. Langford and J. Pineau, editors, Proceedings of the 29th International Conference on Machine Learning (ICML), pages 1255–1262, New York, NY, USA, 2012. Omnipress
  20. ^ P. O. Hoyer, D. Janzing, J. M. Mooij, J. Peters, and B. Schölkopf. Nonlinear causal discovery with additive noise models. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural Information Processing Systems 21, pages 689–696, Red Hook, NY, USA, 2009. Curran
  21. ^ D. Janzing, P. Hoyer, and B. Schölkopf. Telling cause from effect based on high-dimensional observations. In J. Fu ̈rnkranz and T. Joachims, editors, Proceedings of the 27th International Conference on Machine Learning, pages 479–486, Madison, WI, USA, 2010. International Machine Learning Society
  22. ^ J.M. Mooij, J. Peters, D. Janzing, J. Zscheischler, and B. Schölkopf. Distinguishing cause from effect using observational data: methods and benchmarks. Journal of Machine Learning Research, 17(32):1–102, 2016
  23. ^ J. Peters, JM. Mooij, D. Janzing, and B. Schölkopf. Causal discovery with continuous additive noise models. Journal of Machine Learning Research, 15:2009–2053, 2014
  24. ^ P. Daniusis, D. Janzing, J. Mooij, J. Zscheischler, B. Steudel, K. Zhang, and B. Schölkopf. Inferring deterministic causal relations. In P. Grünwald and P. Spirtes, editors, 26th Conference on Uncertainty in Artificial Intelligence, pages 143–150, Corvallis, OR, 2010. AUAI Press. Best student paper award
  25. ^ Janzing, Dominik; Schölkopf, Bernhard (6 October 2010). "Causal Inference Using the Algorithmic Markov Condition". IEEE Transactions on Information Theory. 56 (10): 5168–5194. arXiv:0804.3678. doi:10.1109/TIT.2010.2060095. S2CID 11867432 – via IEEE Xplore.
  26. ^ https://icml.cc/2012/papers/625.pdf[bare URL PDF]
  27. ^ "From kernels to causal inference". videolectures.net.
  28. ^ "Causal Learning --- Bernhard Schölkopf". 15 October 2017 – via Vimeo.
  29. ^ B. Schölkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang, and J. Mooij. On causal and anticausal learning. In J. Langford and J. Pineau, editors, Proceedings of the 29th International Conference on Machine Learning (ICML), pages 1255–1262, New York, NY, USA, 2012. Omnipress
  30. ^ K. Zhang, B. Schölkopf, K. Muandet, and Z. Wang. Domain adaptation under target and conditional shift. In S. Dasgupta and D. McAllester, editors, Proceedings of the 30th International Conference on Machine Learning, volume 28 of JMLR Workshop and Conference Proceedings, pages 819–827, 2013
  31. ^ Schölkopf, Bernhard (6 February 2015). "Learning to see and act". Nature. 518 (7540): 486–487. doi:10.1038/518486a. PMID 25719660. S2CID 4461791 – via www.nature.com.
  32. ^ Schölkopf, Bernhard; Hogg, David W.; Wang, Dun; Foreman-Mackey, Daniel; Janzing, Dominik; Simon-Gabriel, Carl-Johann; Peters, Jonas (5 July 2016). "Modeling confounding by half-sibling regression". Proceedings of the National Academy of Sciences. 113 (27): 7391–7398. Bibcode:2016PNAS..113.7391S. doi:10.1073/pnas.1511656113. PMC 4941423. PMID 27382154.
  33. ^ D. Foreman-Mackey, B. T. Montet, D. W. Hogg, T. D. Morton, D. Wang, and B. Schölkopf. A systematic search for transiting planets in the K2 data. The Astrophysical Journal, 806(2), 2015
  34. ^ https://www.leopoldina.org/fileadmin/redaktion/Mitglieder/CV_Schoelkopf_Bernhard_D.pdf[bare URL PDF]
  35. ^ "TU Berlin – Medieninformation Nr. 209 – 17. September 1998". archiv.pressestelle.tu-berlin.de.
  36. ^ "History of the Institute". www.kyb.tuebingen.mpg.de.
  37. ^ https://www.mpg.de/4379702/MPR_2011_2.pdf[bare URL PDF]
  38. ^ "Machine Learning Summer Schools – MLSS". mlss.cc.
  39. ^ "Cambridge Machine Learning Group | PhD Programme in Advanced Machine Learning".
  40. ^ Williams, Jonathan. "Max Planck ETH Center for Learning Systems". cls-staging.is.localnet.
  41. ^ "Service". Baden-Württemberg.de.
  42. ^ https://standards.ieee.org/content/dam/ieee-standards/standards/web/documents/other/ead_v1.pdf[bare URL PDF]
  43. ^ "World's Top Computer Scientists: H-Index Computer Science Ranking". www.guide2research.com.
  44. ^ http://people.tuebingen.mpg.de/bs/alumni.htm
  45. ^ Williams, Jon. "Bernhard Schölkopf receives Frontiers of Knowledge Award | Empirical Inference". Max Planck Institute for Intelligent Systems.

External links

 
Scholia has an author profile for Bernhard Schölkopf.

April 24, 2023
bernhard, schölkopf, this, article, uses, bare, urls, which, uninformative, vulnerable, link, please, consider, converting, them, full, citations, ensure, article, remains, verifiable, maintains, consistent, citation, style, several, templates, tools, availabl. This article uses bare URLs which are uninformative and vulnerable to link rot Please consider converting them to full citations to ensure the article remains verifiable and maintains a consistent citation style Several templates and tools are available to assist in formatting such as Reflinks documentation reFill documentation and Citation bot documentation June 2022 Learn how and when to remove this template message Bernhard Scholkopf is a German computer scientist born 20 February 1968 known for his work in machine learning especially on kernel methods and causality He is a director at the Max Planck Institute for Intelligent Systems in Tubingen Germany where he heads the Department of Empirical Inference He is also an affiliated professor at ETH Zurich honorary professor at the University of Tubingen and the Technical University Berlin and chairman of the European Laboratory for Learning and Intelligent Systems ELLIS Bernhard ScholkopfBornFebruary 1968 age 55 Alma materUniversity of London 1992 MSc in Mathematics University of Tubingen 1994 Diplom in Physics TU Berlin 1997 PhD in Computer Science Known forMachine Learning Kernel Methods Causal InferenceAwardsBBVA Foundation Frontiers of Knowledge Awards 2020 Korber European Science Prize 2019 Causality in Statistics Education Award American Statistical Association 1 Leibniz Prize 2018 Fellow of the ACM Association for Computing Machinery 2018 Member of the German National Academy of Science Leopoldina 2017 Milner Award 2014 Academy Prize of the Berlin Brandenburg Academy of Sciences and Humanities 2012 Max Planck Research Award 2011 J K Aggarwal Prize of the International Association for Pattern Recognition 2006 Scientific careerInstitutionsMax Planck Institute for Intelligent Systems Contents 1 Research 1 1 Kernel methods 1 2 Causality 2 Education and employment 3 Awards 4 References 5 External linksResearch EditKernel methods Edit Scholkopf developed SVM methods achieving world record performance on the MNIST pattern recognition benchmark at the time 2 With the introduction of kernel PCA Scholkopf and coauthors argued that SVMs are a special case of a much larger class of methods and all algorithms that can be expressed in terms of dot products can be generalized to a nonlinear setting by means of what is known as reproducing kernels 3 4 Another significant observation was that the data on which the kernel is defined need not be vectorial as long as the kernel Gram matrix is positive definite 5 Both insights together led to the foundation of the field of kernel methods encompassing SVMs and many other algorithms Kernel methods are now textbook knowledge and one of the major machine learning paradigms in research and applications Developing kernel PCA Scholkopf extended it to extract invariant features and to design invariant kernels 6 7 8 and showed how to view other major dimensionality reduction methods such as LLE and Isomap as special cases In further work with Alex Smola and others he extended the SVM method to regression and classification with pre specified sparsity 9 and quantile support estimation 10 He proved a representer theorem implying that SVMs kernel PCA and most other kernel algorithms regularized by a norm in a reproducing kernel Hilbert space have solutions taking the form of kernel expansions on the training data thus reducing an infinite dimensional optimization problem to a finite dimensional one He co developed kernel embeddings of distributions methods to represent probability distributions in Hilbert Spaces 11 12 13 14 with links to Fraunhofer diffraction 15 as well as applications to independence testing 16 17 18 Causality Edit Starting in 2005 Scholkopf turned his attention to causal inference Causal mechanisms in the world give rise to statistical dependencies as epiphenomena but only the latter are exploited by popular machine learning algorithms Knowledge about causal structures and mechanisms is useful by letting us predict not only future data coming from the same source but also the effect of interventions in a system and by facilitating transfer of detected regularities to new situations 19 Scholkopf and co workers addressed and in certain settings solved the problem of causal discovery for the two variable setting 20 21 22 23 24 and connected causality to Kolmogorov complexity 25 Around 2010 Scholkopf began to explore how to use causality for machine learning exploiting assumptions of independence of mechanisms and invariance 26 His early work on causal learning was exposed to a wider machine learning audience during his Posner lecture 27 at NeurIPS 2011 as well as in a keynote talk at ICML 2017 28 He assayed how to exploit underlying causal structures in order to make machine learning methods more robust with respect to distribution shifts 29 30 31 and systematic errors 32 the latter leading to the discovery of a number of new exoplanets 33 including K2 18b which was subsequently found to contain water vapour in its atmosphere a first for an exoplanet in the habitable zone Education and employment EditScholkopf studied mathematics physics and philosophy in Tubingen and London He was supported by the Studienstiftung and won the Lionel Cooper Memorial Prize for the best M Sc in Mathematics at the University of London 34 He completed a Diplom in Physics and then moved to Bell Labs in New Jersey where he worked with Vladimir Vapnik who became co adviser of his PhD thesis at the TU Berlin with Stefan Jahnichen His thesis defended in 1997 won the annual award of the German Informatics Association 35 In 2001 following positions in Berlin Cambridge and New York he founded the Department for Empirical Inference at the Max Planck Institute for Biological Cybernetics which grew into a leading center for research in machine learning In 2011 he became founding director at the Max Planck Institute for Intelligent Systems 36 37 With Alex Smola Scholkopf co founded the series of Machine Learning Summer Schools 38 He also co founded a Cambridge Tubingen PhD Programme 39 and the Max Planck ETH Center for Learning Systems 40 In 2016 he co founded the Cyber Valley research consortium 41 He participated in the IEEE Global Initiative on Ethically Aligned Design 42 Scholkopf is co editor in Chief of the Journal of Machine Learning Research a journal he helped found being part of a mass resignation of the editorial board of Machine Learning journal He is among the world s most cited computer scientists 43 Alumni of his lab include Ulrike von Luxburg Carl Rasmussen Matthias Hein Arthur Gretton Gunnar Ratsch Matthias Bethge Stefanie Jegelka Jason Weston Olivier Bousquet Olivier Chapelle Joaquin Quinonero Candela and Sebastian Nowozin 44 Awards EditScholkopf s awards include the Royal Society Milner Award and shared with Isabelle Guyon and Vladimir Vapnik the BBVA Foundation Frontiers of Knowledge Award in the Information and Communication Technologies category He was the first scientist working in Europe to receive this award 45 References Edit Causality in Statistics Education Award www amstat org Decoste Dennis Scholkopf Bernhard 1 January 2002 Training Invariant Support Vector Machines Machine Learning 46 1 161 190 doi 10 1023 A 1012454411458 hdl 11858 00 001M 0000 0013 E06A A S2CID 85843 via Springer Link https direct mit edu neco article 10 5 1299 6193 Nonlinear Component Analysis as a Kernel https ei is tuebingen mpg de publications 5634 Burges Christopher J C 1 June 1998 A Tutorial on Support Vector Machines for Pattern Recognition Data Mining and Knowledge Discovery 2 2 121 167 doi 10 1023 A 1009715923555 S2CID 221627509 via Springer Link B Scholkopf Support Vector Learning PhD Thesis 1997 https ei is tuebingen mpg de publications 5634 B Scholkopf A J Smola and K R Muller Nonlinear component analysis as a kernel eigenvalue problem Neural Computation 10 5 1299 1319 1998e Scholkopf P Simard A J Smola and V Vapnik Prior knowledge in support vector kernels In M Jordan M Kearns and S Solla editors Advances in Neural Information Processing Systems 10 pages 640 646 Cambridge MA USA 1998d MIT Press Chapelle and B Scholkopf Incorporating invariances in nonlinear SVMs In T G Dietterich S Becker and Z Ghahramani editors Advances in Neural Information Processing Systems 14 pages 609 616 Cambridge MA USA 2002 MIT Press B Scholkopf A J Smola R C Williamson and P L Bartlett New support vector algorithms Neural Computation 12 5 1207 1245 2000a B Scholkopf J C Platt J Shawe Taylor A J Smola and R C Williamson Estimating the support of a high dimensional distribution Neural Computation 13 7 1443 1471 2001b A Gretton K Borgwardt M Rasch B Scholkopf and A Smola A Kernel Method for the Two Sample Problem Advances in Neural Information Processing Systems 19 513 520 2007 A J Smola and A Gretton and L Song and B Scholkopf A Hilbert Space Embedding for Distributions Algorithmic Learning Theory 18th International Conference 13 31 2007 B Sriperumbudur A Gretton K Fukumizu B Scholkopf and G Lanckriet Hilbert Space Embeddings and Metrics on Probability Measures Journal of Machine Learning Research 11 1517 1561 2010 A Gretton K Borgwardt M Rasch B Scholkopf and A J Smola A Kernel Two Sample Test Journal of Machine Learning Research 13 723 773 2012 S Harmeling M Hirsch and B Scholkopf On a link between kernel mean maps and Fraunhofer diffraction with an application to super resolution beyond the diffraction limit In Computer Vision and Pattern Recognition CVPR pages 1083 1090 IEEE 2013 A Gretton R Herbrich A J Smola O Bousquet and B Scholkopf Kernel methods for measuring independence Journal of Machine Learning Research 6 2075 2129 2005a A Gretton O Bousquet A J Smola and B Scholkopf Measuring Statistical Dependence with Hilbert Schmidt Norms Algorithmic Learning Theory 16th International Conference 2005b A Gretton K Fukumizu C H Teo L Song B Scholkopf and A J Smola A Kernel Statistical Test of Independence Advances in Neural Information Processing Systems 20 2007 B Scholkopf D Janzing J Peters E Sgouritsa K Zhang and J Mooij On causal and anticausal learning In J Langford and J Pineau editors Proceedings of the 29th International Conference on Machine Learning ICML pages 1255 1262 New York NY USA 2012 Omnipress P O Hoyer D Janzing J M Mooij J Peters and B Scholkopf Nonlinear causal discovery with additive noise models In D Koller D Schuurmans Y Bengio and L Bottou editors Advances in Neural Information Processing Systems 21 pages 689 696 Red Hook NY USA 2009 Curran D Janzing P Hoyer and B Scholkopf Telling cause from effect based on high dimensional observations In J Fu rnkranz and T Joachims editors Proceedings of the 27th International Conference on Machine Learning pages 479 486 Madison WI USA 2010 International Machine Learning Society J M Mooij J Peters D Janzing J Zscheischler and B Scholkopf Distinguishing cause from effect using observational data methods and benchmarks Journal of Machine Learning Research 17 32 1 102 2016 J Peters JM Mooij D Janzing and B Scholkopf Causal discovery with continuous additive noise models Journal of Machine Learning Research 15 2009 2053 2014 P Daniusis D Janzing J Mooij J Zscheischler B Steudel K Zhang and B Scholkopf Inferring deterministic causal relations In P Grunwald and P Spirtes editors 26th Conference on Uncertainty in Artificial Intelligence pages 143 150 Corvallis OR 2010 AUAI Press Best student paper award Janzing Dominik Scholkopf Bernhard 6 October 2010 Causal Inference Using the Algorithmic Markov Condition IEEE Transactions on Information Theory 56 10 5168 5194 arXiv 0804 3678 doi 10 1109 TIT 2010 2060095 S2CID 11867432 via IEEE Xplore https icml cc 2012 papers 625 pdf bare URL PDF From kernels to causal inference videolectures net Causal Learning Bernhard Scholkopf 15 October 2017 via Vimeo B Scholkopf D Janzing J Peters E Sgouritsa K Zhang and J Mooij On causal and anticausal learning In J Langford and J Pineau editors Proceedings of the 29th International Conference on Machine Learning ICML pages 1255 1262 New York NY USA 2012 Omnipress K Zhang B Scholkopf K Muandet and Z Wang Domain adaptation under target and conditional shift In S Dasgupta and D McAllester editors Proceedings of the 30th International Conference on Machine Learning volume 28 of JMLR Workshop and Conference Proceedings pages 819 827 2013 Scholkopf Bernhard 6 February 2015 Learning to see and act Nature 518 7540 486 487 doi 10 1038 518486a PMID 25719660 S2CID 4461791 via www nature com Scholkopf Bernhard Hogg David W Wang Dun Foreman Mackey Daniel Janzing Dominik Simon Gabriel Carl Johann Peters Jonas 5 July 2016 Modeling confounding by half sibling regression Proceedings of the National Academy of Sciences 113 27 7391 7398 Bibcode 2016PNAS 113 7391S doi 10 1073 pnas 1511656113 PMC 4941423 PMID 27382154 D Foreman Mackey B T Montet D W Hogg T D Morton D Wang and B Scholkopf A systematic search for transiting planets in the K2 data The Astrophysical Journal 806 2 2015 https www leopoldina org fileadmin redaktion Mitglieder CV Schoelkopf Bernhard D pdf bare URL PDF TU Berlin Medieninformation Nr 209 17 September 1998 archiv pressestelle tu berlin de History of the Institute www kyb tuebingen mpg de https www mpg de 4379702 MPR 2011 2 pdf bare URL PDF Machine Learning Summer Schools MLSS mlss cc Cambridge Machine Learning Group PhD Programme in Advanced Machine Learning Williams Jonathan Max Planck ETH Center for Learning Systems cls staging is localnet Service Baden Wurttemberg de https standards ieee org content dam ieee standards standards web documents other ead v1 pdf bare URL PDF World s Top Computer Scientists H Index Computer Science Ranking www guide2research com http people tuebingen mpg de bs alumni htm Williams Jon Bernhard Scholkopf receives Frontiers of Knowledge Award Empirical Inference Max Planck Institute for Intelligent Systems External links Edit Scholia has an author profile for Bernhard Scholkopf Bernhard Scholkopf publications indexed by Google Scholar Retrieved from https en wikipedia org w index php title Bernhard Scholkopf amp oldid 1150921157, wikipedia, wiki, book, books, library,

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