| Combining online and offline knowledge in UCT S Gelly, D Silver Proceedings of the 24th international conference on Machine learning, 273-280, 2007 | 674 | 2007 |
| Modification of UCT with patterns in Monte-Carlo Go S Gelly, Y Wang, R Munos, O Teytaud INRIA, 2006 | 490 | 2006 |
| Modification of UCT with patterns in Monte-Carlo Go S Gelly, Y Wang, R Munos, O Teytaud INRIA, 2006 | 490 | 2006 |
| Are gans created equal? a large-scale study M Lucic, K Kurach, M Michalski, S Gelly, O Bousquet Advances in neural information processing systems, 700-709, 2018 | 487 | 2018 |
| Wasserstein auto-encoders I Tolstikhin, O Bousquet, S Gelly, B Schoelkopf arXiv preprint arXiv:1711.01558, 2017 | 477 | 2017 |
| Monte-Carlo tree search and rapid action value estimation in computer Go S Gelly, D Silver Artificial Intelligence 175 (11), 1856-1875, 2011 | 353 | 2011 |
| Challenging common assumptions in the unsupervised learning of disentangled representations F Locatello, S Bauer, M Lucic, G Raetsch, S Gelly, B Schölkopf, O Bachem international conference on machine learning, 4114-4124, 2019 | 313 | 2019 |
| Exploration exploitation in go: UCT for Monte-Carlo go S Gelly, Y Wang | 268 | 2006 |
| Exploration exploitation in go: UCT for Monte-Carlo go S Gelly, Y Wang | 268 | 2006 |
| The grand challenge of computer Go: Monte Carlo tree search and extensions S Gelly, L Kocsis, M Schoenauer, M Sebag, D Silver, C Szepesvári, ... Communications of the ACM 55 (3), 106-113, 2012 | 245 | 2012 |
| Achieving master level play in 9 x 9 computer go. S Gelly, D Silver AAAI 8, 1537-1540, 2008 | 159 | 2008 |
| Modifications of UCT and sequence-like simulations for Monte-Carlo Go Y Wang, S Gelly 2007 IEEE Symposium on Computational Intelligence and Games, 175-182, 2007 | 159 | 2007 |
| Adagan: Boosting generative models IO Tolstikhin, S Gelly, O Bousquet, CJ Simon-Gabriel, B Schölkopf Advances in neural information processing systems, 5424-5433, 2017 | 158 | 2017 |
| Parameter-efficient transfer learning for NLP N Houlsby, A Giurgiu, S Jastrzebski, B Morrone, Q De Laroussilhe, ... arXiv preprint arXiv:1902.00751, 2019 | 113 | 2019 |
| Assessing generative models via precision and recall MSM Sajjadi, O Bachem, M Lucic, O Bousquet, S Gelly Advances in Neural Information Processing Systems, 5228-5237, 2018 | 112 | 2018 |
| The gan landscape: Losses, architectures, regularization, and normalization K Kurach, M Lucic, X Zhai, M Michalski, S Gelly | 98 | 2018 |
| On mutual information maximization for representation learning M Tschannen, J Djolonga, PK Rubenstein, S Gelly, M Lucic arXiv preprint arXiv:1907.13625, 2019 | 89 | 2019 |
| Multi-armed bandit, dynamic environments and meta-bandits C Hartland, S Gelly, N Baskiotis, O Teytaud, M Sebag | 86 | 2006 |
| Episodic curiosity through reachability N Savinov, A Raichuk, R Marinier, D Vincent, M Pollefeys, T Lillicrap, ... arXiv preprint arXiv:1810.02274, 2018 | 84 | 2018 |
| From optimal transport to generative modeling: the VEGAN cookbook O Bousquet, S Gelly, I Tolstikhin, CJ Simon-Gabriel, B Schoelkopf arXiv preprint arXiv:1705.07642, 2017 | 79 | 2017 |
