Krikamol Muandet

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Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Kenji FukumizuThe Institute of Statistical MathematicsVerified email at ism.ac.jp
Bharath SriperumbudurPennsylvania State UniversityVerified email at psu.edu
Kun ZhangCarnegie Mellon University & Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)Verified email at cmu.edu
Arthur GrettonGatsby Computational Neuroscience Unit and Google DeepmindVerified email at gatsby.ucl.ac.uk
Siyu TangETH ZürichVerified email at inf.ethz.ch
Zhikun WangGoogleVerified email at google.com
Yan ZhangETH ZurichVerified email at inf.ethz.ch
David BalduzziXTX MarketsVerified email at google.com
Jonas M. KüblerAmazon Web ServicesVerified email at tuebingen.mpg.de
Junhyung Park3BIGS, Co, Ltd.Verified email at 3bigs.com
Wittawat JitkrittumGoogle ResearchVerified email at google.com
Ilya TolstikhinGoogle DeepmindVerified email at google.com
David Lopez-PazMeta AIVerified email at fb.com
Francesco DinuzzoMetaVerified email at fb.com
Arash MehrjouETH Zürich - Max Planck InstituteVerified email at tuebingen.mpg.de
Stefan KlusHeriot-Watt UniversityVerified email at hw.ac.uk
Niki KilbertusTechnical University of Munich & Helmholtz AIVerified email at tum.de
sanparith marukatatNECTECVerified email at nectec.or.th
Heiko NeumannProfessor for Computer Science, Ulm University, Institute of Neural Information ProcessingVerified email at uni-ulm.de

Krikamol Muandet

CISPA - Helmholtz Center for Information Security

Verified email at cispa.de - Homepage

Kernel Methods Counterfactual Learning Statistical Learning Theory Machine Learning Artificial Intelligence


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Domain generalization via invariant feature representation K Muandet, D Balduzzi, B Schölkopf International conference on machine learning, 10-18, 2013	1025	2013
Kernel mean embedding of distributions: A review and beyond K Muandet, K Fukumizu, B Sriperumbudur, B Schölkopf Foundations and Trends® in Machine Learning 10 (1-2), 1-141, 2017	689	2017
Domain adaptation under target and conditional shift K Zhang, B Schölkopf, K Muandet, Z Wang International conference on machine learning, 819-827, 2013	658	2013
Learning from distributions via support measure machines K Muandet, K Fukumizu, F Dinuzzo, B Schölkopf Advances in neural information processing systems 25, 2012	219	2012
Towards a learning theory of cause-effect inference D Lopez-Paz, K Muandet, B Schölkopf, I Tolstikhin International Conference on Machine Learning, 1452-1461, 2015	191	2015
Grasping field: Learning implicit representations for human grasps K Karunratanakul, J Yang, Y Zhang, MJ Black, K Muandet, S Tang 2020 International Conference on 3D Vision (3DV), 333-344, 2020	143	2020
A Permutation-Based Kernel Conditional Independence Test. G Doran, K Muandet, K Zhang, B Schölkopf UAI, 132-141, 2014	133	2014
Eigendecompositions of transfer operators in reproducing kernel Hilbert spaces S Klus, I Schuster, K Muandet Journal of Nonlinear Science 30, 283-315, 2020	112	2020
Design and analysis of the NIPS 2016 review process N Shah, B Tabibian, K Muandet, I Guyon, U Von Luxburg Journal of machine learning research 19, 1-34, 2018	101	2018
One-class support measure machines for group anomaly detection K Muandet, B Schölkopf arXiv preprint arXiv:1303.0309, 2013	97	2013
Dual instrumental variable regression K Muandet, A Mehrjou, SK Lee, A Raj Advances in Neural Information Processing Systems 33, 2710-2721, 2020	74	2020
Minimax estimation of kernel mean embeddings I Tolstikhin, BK Sriperumbudur, K Muandet The Journal of Machine Learning Research 18 (1), 3002-3048, 2017	69	2017
Fair decisions despite imperfect predictions N Kilbertus, MG Rodriguez, B Schölkopf, K Muandet, I Valera International Conference on Artificial Intelligence and Statistics, 277-287, 2020	60	2020
Fair decisions despite imperfect predictions N Kilbertus, MG Rodriguez, B Schölkopf, K Muandet, I Valera International Conference on Artificial Intelligence and Statistics, 277-287, 2020	60	2020
A measure-theoretic approach to kernel conditional mean embeddings J Park, K Muandet Advances in neural information processing systems 33, 21247-21259, 2020	58	2020
Kernel mean shrinkage estimators K Muandet, B Sriperumbudur, K Fukumizu, A Gretton, B Schölkopf Journal of Machine Learning Research 17, 2016	54	2016
Regularization, optimization, kernels, and support vector machines JAK Suykens, M Signoretto, A Argyriou CRC Press, 2014	52	2014
Proximal causal learning with kernels: Two-stage estimation and moment restriction A Mastouri, Y Zhu, L Gultchin, A Korba, R Silva, M Kusner, A Gretton, ... International conference on machine learning, 7512-7523, 2021	51	2021
Empirical inference: Festschrift in honor of Vladimir N. Vapnik B Schölkopf, Z Luo, V Vovk Springer Science & Business Media, 2013	45	2013
Kernel mean estimation and Stein effect K Muandet, K Fukumizu, B Sriperumbudur, A Gretton, B Schölkopf International Conference on Machine Learning, 10-18, 2014	43	2014

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