| Bootstrap your own latent-a new approach to self-supervised learning JB Grill, F Strub, F Altché, C Tallec, P Richemond, E Buchatskaya, ... Advances in neural information processing systems 33, 21271-21284, 2020 | 7363 | 2020 |
| Rainbow: Combining improvements in deep reinforcement learning M Hessel, J Modayil, H Van Hasselt, T Schaul, G Ostrovski, W Dabney, ... Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 2940 | 2018 |
| Minimax regret bounds for reinforcement learning MG Azar, I Osband, R Munos International conference on machine learning, 263-272, 2017 | 898 | 2017 |
| koray kavukcuoglu, Remi Munos, and Michal Valko. Bootstrap your own latent-a new approach to self-supervised learning JB Grill, F Strub, F Altché, C Tallec, P Richemond, E Buchatskaya, ... Advances in neural information processing systems 33, 21271-21284, 2020 | 527 | 2020 |
| Large-scale representation learning on graphs via bootstrapping S Thakoor, C Tallec, MG Azar, M Azabou, EL Dyer, R Munos, P Veličković, ... arXiv preprint arXiv:2102.06514, 2021 | 523* | 2021 |
| A general theoretical paradigm to understand learning from human preferences MG Azar, ZD Guo, B Piot, R Munos, M Rowland, M Valko, D Calandriello International Conference on Artificial Intelligence and Statistics, 4447-4455, 2024 | 347 | 2024 |
| Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model M Gheshlaghi Azar, R Munos, HJ Kappen Machine learning 91, 325-349, 2013 | 325 | 2013 |
| Speedy Q-Learning MG Azar, M Ghavamzadeh, HJ Kappen, R Munos Advances in Neural Information Processing Systems, 2411-2419, 2011 | 221* | 2011 |
| The reactor: A fast and sample-efficient actor-critic agent for reinforcement learning A Gruslys, W Dabney, MG Azar, B Piot, M Bellemare, R Munos arXiv preprint arXiv:1704.04651, 2017 | 184* | 2017 |
| Bootstrap latent-predictive representations for multitask reinforcement learning ZD Guo, BA Pires, B Piot, JB Grill, F Altché, R Munos, MG Azar International Conference on Machine Learning, 3875-3886, 2020 | 168 | 2020 |
| Dynamic Policy Programming M Gheshlaghi Azar, V Gomez, HJ Kappen Journal of Machine Learning Research 13, 3207-3245, 2012 | 157 | 2012 |
| Observe and look further: Achieving consistent performance on atari T Pohlen, B Piot, T Hester, MG Azar, D Horgan, D Budden, G Barth-Maron, ... arXiv preprint arXiv:1805.11593, 2018 | 145 | 2018 |
| Sequential transfer in multi-armed bandit with finite set of models MG Azar, A Lazaric, E Brunskill Advances in Neural Information Processing Systems, 2220-2228, 2013 | 128 | 2013 |
| On the sample complexity of reinforcement learning with a generative model MG Azar, R Munos, B Kappen arXiv preprint arXiv:1206.6461, 2012 | 119 | 2012 |
| Hindsight credit assignment A Harutyunyan, W Dabney, T Mesnard, M Gheshlaghi Azar, B Piot, ... Advances in neural information processing systems 32, 2019 | 103 | 2019 |
| Meta-learning of sequential strategies PA Ortega, JX Wang, M Rowland, T Genewein, Z Kurth-Nelson, ... arXiv preprint arXiv:1905.03030, 2019 | 101 | 2019 |
| Neural predictive belief representations ZD Guo, MG Azar, B Piot, BA Pires, R Munos arXiv preprint arXiv:1811.06407, 2018 | 95 | 2018 |
| Nash learning from human feedback R Munos, M Valko, D Calandriello, MG Azar, M Rowland, ZD Guo, Y Tang, ... arXiv preprint arXiv:2312.00886, 2023 | 89 | 2023 |
| Byol-explore: Exploration by bootstrapped prediction Z Guo, S Thakoor, M Pîslar, B Avila Pires, F Altché, C Tallec, A Saade, ... Advances in neural information processing systems 35, 31855-31870, 2022 | 72 | 2022 |
| Stochastic optimization of a locally smooth function under correlated bandit feedback MG Azar, A Lazaric, E Brunskill 31st International Conference on Machine Learning (ICML), 2014 | 71* | 2014 |
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