| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 3445 | 2023 |
| Gemma: Open models based on gemini research and technology G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ... arXiv preprint arXiv:2403.08295, 2024 | 1276* | 2024 |
| Gemma 2: Improving open language models at a practical size G Team, M Riviere, S Pathak, PG Sessa, C Hardin, S Bhupatiraju, ... arXiv preprint arXiv:2408.00118, 2024 | 589* | 2024 |
| What matters for on-policy deep actor-critic methods? a largescale study M Andrychowicz, A Raichuk, P Stanczyk, M Orsini, S Girgin, R Marinier, ... International Conference on Learning Representations (ICLR), 2021, 2021 | 521* | 2021 |
| Acme: A research framework for distributed reinforcement learning MW Hoffman, B Shahriari, J Aslanides, G Barth-Maron, N Momchev, ... arXiv preprint arXiv:2006.00979, 2022 | 277 | 2022 |
| Primal wasserstein imitation learning R Dadashi, L Hussenot, M Geist, O Pietquin International Conference on Learning Representations (ICLR), 2021, 2020 | 156 | 2020 |
| What Matters for Adversarial Imitation Learning? M Orsini*, A Raichuk*, L Hussenot*, D Vincent, R Dadashi, S Girgin, ... NeurIPS 35th Conference on Neural Information Processing Systems, 2021 | 88 | 2021 |
| Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback P Roit, J Ferret, L Shani, R Aharoni, G Cideron, R Dadashi, M Geist, ... ACL 2023 Proceedings, forthcoming. Association for Computational Linguistics, 2023 | 79 | 2023 |
| Warm: On the benefits of weight averaged reward models A Ramé, N Vieillard, L Hussenot, R Dadashi, G Cideron, O Bachem, ... arXiv preprint arXiv:2401.12187, 2024 | 72 | 2024 |
| Offline Reinforcement Learning as Anti-Exploration S Rezaeifar, R Dadashi, N Vieillard, L Hussenot, O Bachem, O Pietquin, ... AAAI 2022, 2021 | 65 | 2021 |
| Offline Reinforcement Learning with Pseudometric Learning R Dadashi, S Rezaeifar, N Vieillard, L Hussenot, O Pietquin, M Geist International Conference on Machine Learning (ICML), 2021, 2021 | 45 | 2021 |
| Continuous Control with Action Quantization from Demonstrations R Dadashi*, L Hussenot*, D Vincent, S Girgin, A Raichuk, M Geist, ... International Conference on Machine Learning (ICML), 2022, 2021 | 35 | 2021 |
| CopyCAT: Taking control of neural policies with constant attacks L Hussenot, M Geist, O Pietquin International Conference on Autonomous Agents and Multiagent Systems (AAMAS …, 2019 | 34 | 2019 |
| Bond: Aligning llms with best-of-n distillation PG Sessa, R Dadashi, L Hussenot, J Ferret, N Vieillard, A Ramé, ... arXiv preprint arXiv:2407.14622, 2024 | 24* | 2024 |
| Targeted attacks on deep reinforcement learning agents through adversarial observations L Hussenot, M Geist, O Pietquin ICML Workshop, 2020 | 24 | 2020 |
| Hyperparameter Selection for Imitation Learning L Hussenot, M Andrychowicz, D Vincent, R Dadashi, A Raichuk, ... International Conference on Machine Learning (ICML), 2021, 2021 | 23 | 2021 |
| Warp: On the benefits of weight averaged rewarded policies A Ramé, J Ferret, N Vieillard, R Dadashi, L Hussenot, PL Cedoz, ... arXiv preprint arXiv:2406.16768, 2024 | 18* | 2024 |
| Musicrl: Aligning music generation to human preferences G Cideron, S Girgin, M Verzetti, D Vincent, M Kastelic, Z Borsos, ... arXiv preprint arXiv:2402.04229, 2024 | 16 | 2024 |
| Rlds: an ecosystem to generate, share and use datasets in reinforcement learning S Ramos, S Girgin, L Hussenot, D Vincent, H Yakubovich, D Toyama, ... arXiv preprint arXiv:2111.02767, 2021 | 16 | 2021 |
| Conditional Language Policy: A General Framework for Steerable Multi-Objective Finetuning K Wang, R Kidambi, R Sullivan, A Agarwal, C Dann, A Michi, M Gelmi, ... arXiv preprint arXiv:2407.15762, 2024 | 13 | 2024 |
Olivier PietquinEarth Species Project | ex Google DeepMind (On leave - Professor at University of Lille)E-mailová adresa ověřena na: univ-lille.fr
