| Bootstrap your own latent: A new approach to self-supervised learning JB Grill, F Strub, F Altché, C Tallec, PH Richemond, E Buchatskaya, ... Neural Information Processing Systems, 2020 | 7582 | 2020 |
| The Llama 3 herd of models A Dubey, A Jauhri, A Pandey, A Kadian, A Al-Dahle, A Letman, A Mathur, ... arXiv preprint arXiv:2407.21783, 2025 | 3571* | 2025 |
| Large-scale representation learning on graphs via bootstrapping S Thakoor, C Tallec, MG Azar, R Munos, P Veličković, M Valko International Conference on Learning Representations, 2022 | 548* | 2022 |
| A general theoretical paradigm to understand learning from human preferences MG Azar, M Rowland, B Piot, D Guo, D Calandriello, M Valko, R Munos International Conference on Artificial Intelligence and Statistics, 2024 | 439 | 2024 |
| Finite-time analysis of kernelised contextual bandits M Valko, N Korda, R Munos, I Flaounas, N Cristianini Uncertainty in Artificial Intelligence, 2013 | 307 | 2013 |
| Outlier detection for patient monitoring and alerting M Hauskrecht, I Batal, M Valko, S Visweswaran, GF Cooper, G Clermont Journal of Biomedical Informatics, 2013 | 184 | 2013 |
| Online influence maximization under independent cascade model with semi-bandit feedback Z Wen, B Kveton, M Valko, S Vaswani Neural Information Processing Systems, 2017 | 168* | 2017 |
| Broaden your views for self-supervised video learning A Recasens, P Luc, JB Alayrac, L Wang, F Strub, C Tallec, M Malinowski, ... International Conference on Computer Vision, 2021 | 148 | 2021 |
| Efficient learning by implicit exploration in bandit problems with side observations T Kocák, G Neu, M Valko, R Munos Neural Information Processing Systems, 2014 | 141 | 2014 |
| Stochastic simultaneous optimistic optimization M Valko, A Carpentier, R Munos International Conference on Machine Learning, 2013 | 140 | 2013 |
| Spectral bandits for smooth graph functions M Valko, R Munos, B Kveton, T Kocák International Conference on Machine Learning, 2014 | 139 | 2014 |
| Episodic reinforcement learning in finite MDPs: Minimax lower bounds revisited O Darwiche Domingues, P Ménard, E Kaufmann, M Valko Algorithmic Learning Theory, 2021 | 131 | 2021 |
| Nash learning from human feedback R Munos, M Valko, D Calandriello, MG Azar, M Rowland, D Guo, Y Tang, ... International Conference on Machine Learning, 2024 | 120 | 2024 |
| Black-box optimization of noisy functions with unknown smoothness JB Grill, M Valko, R Munos Neural Information Processing Systems, 2015 | 116 | 2015 |
| Game Plan: What AI can do for Football, and What Football can do for AI K Tuyls, S Omidshafiei, P Muller, Z Wang, J Connor, D Hennes, I Graham, ... Journal of Artificial Intelligence Research 71, 41-88, 2021 | 115 | 2021 |
| BYOL works even without batch statistics PH Richemond, JB Grill, F Altché, C Tallec, F Strub, A Brock, S Smith, ... NeurIPS 2020 Workshop: Self-Supervised Learning - Theory and Practice, 2020 | 113 | 2020 |
| Simple regret for infinitely many armed bandits A Carpentier, M Valko International Conference on Machine Learning, 2015 | 113 | 2015 |
| Gamification of pure exploration for linear bandits R Degenne, P Ménard, X Shang, M Valko International Conference on Machine Learning, 2020 | 105 | 2020 |
| Adaptive reward-free exploration E Kaufmann, P Ménard, OD Domingues, A Jonsson, E Leurent, M Valko Algorithmic Learning Theory, 2021 | 101 | 2021 |
| Fast active learning for pure exploration in reinforcement learning P Ménard, OD Domingues, A Jonsson, E Kaufmann, E Leurent, M Valko International Conference on Machine Learning, 2021 | 95 | 2021 |
Rémi MunosFAIR, MetaVerified email at inria.fr
