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
Bharath SriperumbudurPennsylvania State UniversityVerified email at psu.edu
Kenji FukumizuThe Institute of Statistical MathematicsVerified email at ism.ac.jp
Kun ZhangCarnegie Mellon University & Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)Verified email at cmu.edu
Siyu TangETH ZürichVerified email at inf.ethz.ch
Arthur GrettonGatsby Computational Neuroscience Unit and Google DeepmindVerified email at gatsby.ucl.ac.uk
Zhikun WangGoogleVerified email at google.com
Yan ZhangMeshcapadeVerified email at inf.ethz.ch
David BalduzziXTX MarketsVerified email at google.com
Jonas M. KüblerAmazon Web ServicesVerified email at tuebingen.mpg.de
Ilya TolstikhinGoogle DeepmindVerified email at google.com
David Lopez-PazAI at MetaVerified email at fb.com
Wittawat JitkrittumGoogle ResearchVerified email at google.com
Junhyung Park3BIGS, Co, Ltd.Verified email at 3bigs.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 MunichVerified email at tum.de
Heiko NeumannProfessor for Computer Science, Ulm University, Institute of Neural Information ProcessingVerified email at uni-ulm.de
Michael J. BlackMax Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de

Krikamol Muandet

CISPA - Helmholtz Center for Information Security

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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	1267	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	861	2017
Domain adaptation under target and conditional shift K Zhang, B Schölkopf, K Muandet, Z Wang International conference on machine learning, 819-827, 2013	747	2013
Learning from distributions via support measure machines K Muandet, K Fukumizu, F Dinuzzo, B Schölkopf Advances in neural information processing systems 25, 2012	231	2012
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	212	2020
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	211	2015
A Permutation-Based Kernel Conditional Independence Test. G Doran, K Muandet, K Zhang, B Schölkopf UAI, 132-141, 2014	148	2014
Eigendecompositions of transfer operators in reproducing kernel Hilbert spaces S Klus, I Schuster, K Muandet Journal of Nonlinear Science 30, 283-315, 2020	139	2020
Design and analysis of the NIPS 2016 review process NB Shah, B Tabibian, K Muandet, I Guyon, U Von Luxburg Journal of machine learning research 19 (49), 1-34, 2018	114	2018
One-class support measure machines for group anomaly detection K Muandet, B Schölkopf arXiv preprint arXiv:1303.0309, 2013	104	2013
Dual instrumental variable regression K Muandet, A Mehrjou, SK Lee, A Raj Advances in Neural Information Processing Systems 33, 2710-2721, 2020	98	2020
A measure-theoretic approach to kernel conditional mean embeddings J Park, K Muandet Advances in neural information processing systems 33, 21247-21259, 2020	80	2020
Minimax estimation of kernel mean embeddings I Tolstikhin, BK Sriperumbudur, K Mu Journal of Machine Learning Research 18 (86), 1-47, 2017	80	2017
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	71	2021
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	69	2020
Regularization, optimization, kernels, and support vector machines JAK Suykens, M Signoretto, A Argyriou CRC Press, 2014	61	2014
Kernel mean shrinkage estimators K Mu, B Sriperumbudur, K Fukumizu, A Gretton, B Schölkopf Journal of Machine Learning Research 17 (48), 1-41, 2016	58	2016
Kernel mean estimation and Stein effect K Muandet, K Fukumizu, B Sriperumbudur, A Gretton, B Schölkopf International Conference on Machine Learning, 10-18, 2014	47	2014
Empirical inference: Festschrift in honor of Vladimir N. Vapnik B Schölkopf, Z Luo, V Vovk Springer Science & Business Media, 2013	45	2013
Computing functions of random variables via reproducing kernel Hilbert space representations B Schölkopf, K Muandet, K Fukumizu, S Harmeling, J Peters Statistics and Computing 25, 755-766, 2015	41	2015

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