| 456 Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap 457 your own latent: A new approach to self-supervised learning JB Grill, F Strub, F Altché, C Tallec, PH Richemond, E Buchatskaya arXiv preprint arXiv:2006.07733 4, 458, 2020 | 2 | 2020 |
| A cryptography-based approach for movement decoding EL Dyer, M Gheshlaghi Azar, MG Perich, HL Fernandes, S Naufel, ... Nature biomedical engineering 1 (12), 967-976, 2017 | 63 | 2017 |
| 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 | 165 | 2024 |
| A General Theoretical Paradigm to Understand Learning from Human Preferences M Gheshlaghi Azar, M Rowland, B Piot, D Guo, D Calandriello, M Valko, ... arXiv e-prints, arXiv: 2310.12036, 2023 | | 2023 |
| An analysis of quantile temporal-difference learning M Rowland, R Munos, MG Azar, Y Tang, G Ostrovski, A Harutyunyan, ... Journal of Machine Learning Research 25, 1-47, 2024 | 16 | 2024 |
| Averaging log-likelihoods in direct alignment N Grinsztajn, Y Flet-Berliac, MG Azar, F Strub, B Wu, E Choi, C Cremer, ... arXiv preprint arXiv:2406.19188, 2024 | | 2024 |
| Blade: Robust exploration via diffusion models B Piot, ZD Guo, S Thakoor, MG Azar Deep Reinforcement Learning Workshop NeurIPS 2022, 2022 | 3 | 2022 |
| 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 | 147 | 2020 |
| 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 | 6185 | 2020 |
| Bootstrapped representation learning on graphs S Thakoor, C Tallec, MG Azar, R Munos, P Veličković, M Valko ICLR 2021 Workshop on Geometrical and Topological Representation Learning, 2021 | | 2021 |
| Bootstrapped representation learning on graphs, 2021 S Thakoor, C Tallec, MG Azar, R Munos, P Velickovic, M Valko URL: https://openreview. net/forum, 2021 | 2 | 2021 |
| Bootstrapped Representation Learning on Graphs. arXiv (2021) S Thakoor, C Tallec, MG Azar, R Munos, P Velickovic, M Valko arXiv preprint arXiv:2102.06514, 2021 | 2 | 2021 |
| 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 | 58 | 2022 |
| Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion Y Flet-Berliac, N Grinsztajn, F Strub, E Choi, C Cremer, A Ahmadian, ... arXiv preprint arXiv:2406.19185, 2024 | | 2024 |
| Controlling an agent to explore an environment using observation likelihoods B Piot, BA Pires, MG Azar US Patent App. 17/425,200, 2022 | 5 | 2022 |
| Convex Relaxation Regression Systems and Related Methods MG Azar, E Dyer, K Kording US Patent App. 16/113,035, 2019 | 2 | 2019 |
| Convex Relaxation Regression Systems and Related Methods MG Azar, E Dyer, K Kording US Patent App. 15/400,941, 2017 | 5 | 2017 |
| Convex relaxation regression: Black-box optimization of smooth functions by learning their convex envelopes MG Azar, E Dyer, K Kording arXiv preprint arXiv:1602.02191, 2016 | 4 | 2016 |
| Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes M Gheshlaghi Azar, E Dyer, K Kording arXiv e-prints, arXiv: 1602.02191, 2016 | | 2016 |
| Correcting Multivariate Auto-Regressive Models for the Influence of Unobserved Common Input V Gómez, M Gheshlaghi Azar, HJ Kappen Artificial Intelligence Research and Development, 177-186, 2016 | | 2016 |
Rémi MunosGoogle DeepMindVerified email at inria.fr