| 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 | 7552 | 2020 |
| 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 | 530 | 2020 |
| Data distributional properties drive emergent in-context learning in transformers S Chan, A Santoro, A Lampinen, J Wang, A Singh, P Richemond, ... Advances in neural information processing systems 35, 18878-18891, 2022 | 318 | 2022 |
| Byol works even without batch statistics PH Richemond, JB Grill, F Altché, C Tallec, F Strub, A Brock, S Smith, ... arXiv preprint arXiv:2010.10241, 2020 | 112 | 2020 |
| Continuous diffusion for categorical data S Dieleman, L Sartran, A Roshannai, N Savinov, Y Ganin, PH Richemond, ... arXiv preprint arXiv:2211.15089, 2022 | 97 | 2022 |
| Generalized preference optimization: A unified approach to offline alignment Y Tang, ZD Guo, Z Zheng, D Calandriello, R Munos, M Rowland, ... arXiv preprint arXiv:2402.05749, 2024 | 72 | 2024 |
| Human alignment of large language models through online preference optimisation D Calandriello, D Guo, R Munos, M Rowland, Y Tang, BA Pires, ... arXiv preprint arXiv:2403.08635, 2024 | 34 | 2024 |
| Understanding self-predictive learning for reinforcement learning Y Tang, ZD Guo, PH Richemond, BA Pires, Y Chandak, R Munos, ... International Conference on Machine Learning, 33632-33656, 2023 | 34 | 2023 |
| Categorical sdes with simplex diffusion PH Richemond, S Dieleman, A Doucet arXiv preprint arXiv:2210.14784, 2022 | 28 | 2022 |
| On Wasserstein reinforcement learning and the Fokker-Planck equation PH Richemond, B Maginnis arXiv preprint arXiv:1712.07185, 2017 | 26 | 2017 |
| Scaling instructable agents across many simulated worlds MA Raad, A Ahuja, C Barros, F Besse, A Bolt, A Bolton, B Brownfield, ... arXiv preprint arXiv:2404.10179, 2024 | 25 | 2024 |
| Zipfian environments for reinforcement learning SCY Chan, AK Lampinen, PH Richemond, F Hill Conference on Lifelong Learning Agents, 406-429, 2022 | 17 | 2022 |
| Offline regularised reinforcement learning for large language models alignment PH Richemond, Y Tang, D Guo, D Calandriello, MG Azar, R Rafailov, ... arXiv preprint arXiv:2405.19107, 2024 | 12 | 2024 |
| Semppl: Predicting pseudo-labels for better contrastive representations M Bošnjak, PH Richemond, N Tomasev, F Strub, JC Walker, F Hill, ... arXiv preprint arXiv:2301.05158, 2023 | 9 | 2023 |
| The edge of orthogonality: A simple view of what makes byol tick PH Richemond, A Tam, Y Tang, F Strub, B Piot, F Hill International Conference on Machine Learning, 29063-29081, 2023 | 8 | 2023 |
| Memory-efficient episodic control reinforcement learning with dynamic online k-means A Agostinelli, K Arulkumaran, M Sarrico, P Richemond, AA Bharath arXiv preprint arXiv:1911.09560, 2019 | 6 | 2019 |
| Sample-efficient reinforcement learning with maximum entropy mellowmax episodic control M Sarrico, K Arulkumaran, A Agostinelli, P Richemond, AA Bharath arXiv preprint arXiv:1911.09615, 2019 | 5 | 2019 |
| Combining learning rate decay and weight decay with complexity gradient descent-Part I PH Richemond, Y Guo arXiv preprint arXiv:1902.02881, 2019 | 5 | 2019 |
| Scaling instructable agents across many simulated worlds M Abi Raad, A Ahuja, C Barros, F Besse, A Bolt, A Bolton, B Brownfield, ... arXiv e-prints, arXiv: 2404.10179, 2024 | 4 | 2024 |
| A short variational proof of equivalence between policy gradients and soft Q learning PH Richemond, B Maginnis arXiv preprint arXiv:1712.08650, 2017 | 4 | 2017 |
