| Domain generalization via invariant feature representation K Muandet, D Balduzzi, B Schölkopf International conference on machine learning, 10-18, 2013 | 1269 | 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 | 862 | 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 | 230 | 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 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETH確認したメール アドレス: tuebingen.mpg.de
