| 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 | 2360 | 2023 |
| Offline reinforcement learning: Tutorial, review, and perspectives on open problems S Levine, A Kumar, G Tucker, J Fu arXiv preprint arXiv:2005.01643, 2020 | 2118 | 2020 |
| Conservative q-learning for offline reinforcement learning A Kumar, A Zhou, G Tucker, S Levine Advances in Neural Information Processing Systems 33, 1179-1191, 2020 | 2015 | 2020 |
| D4rl: Datasets for deep data-driven reinforcement learning J Fu, A Kumar, O Nachum, G Tucker, S Levine arXiv preprint arXiv:2004.07219, 2020 | 1252 | 2020 |
| Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction A Kumar, J Fu, G Tucker, S Levine NeuRIPS 2019, arXiv:1906.00949, 2019 | 1169 | 2019 |
| 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 | 857 | 2024 |
| Advantage-weighted regression: Simple and scalable off-policy reinforcement learning XB Peng, A Kumar, G Zhang, S Levine arXiv preprint arXiv:1910.00177, 2019 | 549 | 2019 |
| Combo: Conservative offline model-based policy optimization T Yu, A Kumar, R Rafailov, A Rajeswaran, S Levine, C Finn Advances in neural information processing systems 34, 28954-28967, 2021 | 450 | 2021 |
| Trainable calibration measures for neural networks from kernel mean embeddings A Kumar, S Sarawagi, U Jain International Conference on Machine Learning, 2805-2814, 2018 | 320 | 2018 |
| Graph Normalizing Flows J Liu, A Kumar, J Ba, J Kiros, K Swersky NeurIPS 2019, arxiv:1905.13177, 2019 | 300* | 2019 |
| Opal: Offline primitive discovery for accelerating offline reinforcement learning A Ajay, A Kumar, P Agrawal, S Levine, O Nachum arXiv preprint arXiv:2010.13611, 2020 | 192 | 2020 |
| Diagnosing Bottlenecks in Deep Q-learning Algorithms J Fu, A Kumar, M Soh, S Levine International Conference on Machine Learning (ICML) 2019, https://arxiv.org …, 2019 | 167 | 2019 |
| Conservative safety critics for exploration H Bharadhwaj, A Kumar, N Rhinehart, S Levine, F Shkurti, A Garg arXiv preprint arXiv:2010.14497, 2020 | 153 | 2020 |
| When should we prefer offline reinforcement learning over behavioral cloning? A Kumar, J Hong, A Singh, S Levine arXiv preprint arXiv:2204.05618, 2022 | 147* | 2022 |
| Discor: Corrective feedback in reinforcement learning via distribution correction A Kumar, A Gupta, S Levine Advances in Neural Information Processing Systems 33, 18560-18572, 2020 | 124 | 2020 |
| Why generalization in rl is difficult: Epistemic pomdps and implicit partial observability D Ghosh, J Rahme, A Kumar, A Zhang, RP Adams, S Levine Advances in neural information processing systems 34, 25502-25515, 2021 | 122 | 2021 |
| Implicit under-parameterization inhibits data-efficient deep reinforcement learning A Kumar, R Agarwal, D Ghosh, S Levine arXiv preprint arXiv:2010.14498, 2020 | 117 | 2020 |
| Cog: Connecting new skills to past experience with offline reinforcement learning A Singh, A Yu, J Yang, J Zhang, A Kumar, S Levine arXiv preprint arXiv:2010.14500, 2020 | 114 | 2020 |
| One solution is not all you need: Few-shot extrapolation via structured maxent rl S Kumar, A Kumar, S Levine, C Finn Advances in Neural Information Processing Systems 33, 8198-8210, 2020 | 102 | 2020 |
| Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters C Snell, J Lee, K Xu, A Kumar arXiv preprint arXiv:2408.03314, 2024 | 101 | 2024 |
Sergey LevineUC Berkeley, Physical IntelligenceDirección de correo verificada de eecs.berkeley.edu
