| 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 | 2070 | 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 | 677 | 2024 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 614 | 2024 |
| Continuous Hierarchical Representations with Poincar\'e Variational Auto-Encoders E Mathieu, C Le Lan, CJ Maddison, R Tomioka, YW Teh Advances in Neural Information Processing Systems (NeurIPS19), 2019 | 226* | 2019 |
| Gemma 2: Improving open language models at a practical size G Team, M Riviere, S Pathak, PG Sessa, C Hardin, S Bhupatiraju, ... arXiv e-prints, arXiv: 2408.00118, 2024 | 178 | 2024 |
| Lietransformer: Equivariant self-attention for lie groups MJ Hutchinson*, C Le Lan*, S Zaidi*, E Dupont, YW Teh, H Kim International Conference on Machine Learning (ICML21), *equal contribution, 2021 | 118* | 2021 |
| Perfect density models cannot guarantee anomaly detection C Le Lan, L Dinh Journal Entropy, ICBINB NeurIPS20 Workshop (Entropic Award,Oral); arXiv …, 2021 | 55 | 2021 |
| On the Generalization of Representations in Reinforcement Learning C Le Lan, S Tu, A Oberman, R Agarwal, MG Bellemare International Conference on Artificial Intelligence and Statistics (AISTATS22), 2022 | 36 | 2022 |
| Metrics and continuity in reinforcement learning C Le Lan, MG Bellemare, PS Castro AAAI Conference on Artificial Intelligence 35, (AAAI21), 2021 | 34 | 2021 |
| Understanding Self-Predictive Learning for Reinforcement Learning Y Tang, ZD Guo, PH Richemond, BÁ Pires, Y Chandak, R Munos, ... International Conference on Machine Learning (ICML23), 2023 | 32 | 2023 |
| Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks J Farebrother*, J Greaves*, R Agarwal, C Le Lan, R Goroshin, PS Castro, ... International Conference on Learning Representations (ICLR23), *equal …, 2023 | 31* | 2023 |
| 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 | 7 | 2024 |
| Bootstrapped Representations in Reinforcement Learning C Le Lan, S Tu, M Rowland, A Harutyunyan, R Agarwal, MG Bellemare, ... International Conference on Machine Learning (ICML23), 2023 | 7 | 2023 |
| A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces C Le Lan, J Greaves, J Farebrother, M Rowland, F Pedregosa, R Agarwal, ... International Conference on Artificial Intelligence and Statistics (AISTATS23), 2023 | 5 | 2023 |
| Revisiting Bisimulation: A Sampling-Based State Similarity Pseudo-metric C Le Lan, R Agarwal ICLR 2023 Tiny Papers, 2023 | 3 | 2023 |
| Understanding representation learning for deep reinforcement learning C Le Lan PhD thesis, 2023 | | 2023 |
Rishabh AgarwalStaff Research Scientist, Google DeepMind. Adjunct Prof, McGillEmail verificata su google.com