| Learning with local and global consistency D Zhou, O Bousquet, T Lal, J Weston, B Schölkopf Advances in neural information processing systems 16, 2003 | 5467 | 2003 |
| Concentration inequalities S Boucheron, G Lugosi, O Bousquet Summer school on machine learning, 208-240, 2003 | 4373 | 2003 |
| Choosing multiple parameters for support vector machines O Chapelle, V Vapnik, O Bousquet, S Mukherjee Machine learning 46, 131-159, 2002 | 3491 | 2002 |
| The tradeoffs of large scale learning L Bottou, O Bousquet Advances in neural information processing systems 20, 2007 | 2222 | 2007 |
| Stability and generalization O Bousquet, A Elisseeff The Journal of Machine Learning Research 2, 499-526, 2002 | 2136 | 2002 |
| Measuring statistical dependence with Hilbert-Schmidt norms A Gretton, O Bousquet, A Smola, B Schölkopf International conference on algorithmic learning theory, 63-77, 2005 | 1959 | 2005 |
| Wasserstein auto-encoders I Tolstikhin, O Bousquet, S Gelly, B Schoelkopf arXiv preprint arXiv:1711.01558, 2017 | 1278 | 2017 |
| Are gans created equal? a large-scale study M Lucic, K Kurach, M Michalski, S Gelly, O Bousquet Advances in neural information processing systems 31, 2018 | 1257 | 2018 |
| Ranking on data manifolds D Zhou, J Weston, A Gretton, O Bousquet, B Schölkopf Advances in neural information processing systems 16, 2003 | 985 | 2003 |
| Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson | 927 | 2005 |
| Introduction to statistical learning theory O Bousquet, S Boucheron, G Lugosi Summer school on machine learning, 169-207, 2003 | 842 | 2003 |
| Least-to-most prompting enables complex reasoning in large language models D Zhou, N Schärli, L Hou, J Wei, N Scales, X Wang, D Schuurmans, C Cui, ... arXiv preprint arXiv:2205.10625, 2022 | 831 | 2022 |
| Theory of classification: A survey of some recent advances S Boucheron, O Bousquet, G Lugosi ESAIM: probability and statistics 9, 323-375, 2005 | 787 | 2005 |
| Consistency of spectral clustering U Von Luxburg, M Belkin, O Bousquet The Annals of Statistics, 555-586, 2008 | 758 | 2008 |
| Assessing generative models via precision and recall MSM Sajjadi, O Bachem, M Lucic, O Bousquet, S Gelly Advances in neural information processing systems 31, 2018 | 582 | 2018 |
| Kernel methods for measuring independence. A Gretton, R Herbrich, A Smola, O Bousquet, B Schölkopf, A Hyvärinen Journal of Machine Learning Research 6 (12), 2005 | 468 | 2005 |
| A Bennett concentration inequality and its application to suprema of empirical processes O Bousquet Comptes Rendus Mathematique 334 (6), 495-500, 2002 | 382 | 2002 |
| Google research football: A novel reinforcement learning environment K Kurach, A Raichuk, P Stańczyk, M Zając, O Bachem, L Espeholt, ... Proceedings of the AAAI conference on artificial intelligence 34 (04), 4501-4510, 2020 | 371 | 2020 |
| The visual task adaptation benchmark X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ... | 370* | 2019 |
| Measuring compositional generalization: A comprehensive method on realistic data D Keysers, N Schärli, N Scales, H Buisman, D Furrer, S Kashubin, ... arXiv preprint arXiv:1912.09713, 2019 | 354 | 2019 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETH在 tuebingen.mpg.de 的电子邮件经过验证
