| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 4957 | 2021 |
| Just train twice: Improving group robustness without training group information EZ Liu*, B Haghgoo*, AS Chen*, A Raghunathan, PW Koh, S Sagawa, ... International Conference on Machine Learning, 6781-6792, 2021 | 548 | 2021 |
| Surgical fine-tuning improves adaptation to distribution shifts Y Lee*, AS Chen*, F Tajwar, A Kumar, H Yao, P Liang, C Finn arXiv preprint arXiv:2210.11466, 2022 | 207 | 2022 |
| Learning generalizable robotic reward functions from" in-the-wild" human videos AS Chen, S Nair, C Finn Robotics: Science and Systems (RSS), 2021 | 130* | 2021 |
| Language-driven representation learning for robotics S Karamcheti, S Nair, AS Chen, T Kollar, C Finn, D Sadigh, P Liang arXiv preprint arXiv:2302.12766, 2023 | 122 | 2023 |
| You Only Live Once: Single-Life Reinforcement Learning AS Chen, A Sharma, S Levine, C Finn Neural Information Processing Systems (NeurIPS), 2022 | 25 | 2022 |
| Batch exploration with examples for scalable robotic reinforcement learning AS Chen*, HJ Nam*, S Nair*, C Finn IEEE Robotics and Automation Letters 6 (3), 4401-4408, 2021 | 24 | 2021 |
| On the opportunities and risks of foundation models. arXiv [Preprint](2021) R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S Arx, ... Source:〈 https://arxiv. org/abs/2108.07258, 0 | 21 | |
| On the opportunities and risks of foundation models. arXiv. 10.48550 R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S Von Arx, ... arXiv preprint arXiv.2108.07258, 2021 | 14 | 2021 |
| On the opportunities and risks of foundation models. arXiv preprint R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 13 | 2021 |
| On the opportunities and risks of foundation models. CoRR abs/2108.07258 (2021) R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... Preprint at https://arxiv. org/abs/2108.07258 2108, 2021 | 13 | 2021 |
| Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features AS Chen, Y Lee, A Setlur, S Levine, C Finn The Twelfth International Conference on Learning Representations, 0 | 13* | |
| Index Divisibility in Dynamical Sequences and Cyclic Orbits Modulo AS Chen, TA Gassert, KE Stange New York Journal of Mathematics 23, 1045–1063, 2017 | 11 | 2017 |
| On the Opportunities and Risks of Foundation Models, 2022. doi: 10.48550 R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv.2108.07258 1, 0 | 10 | |
| Commonsense reasoning for legged robot adaptation with vision-language models AS Chen, AM Lessing, A Tang, G Chada, L Smith, S Levine, C Finn arXiv preprint arXiv:2407.02666, 2024 | 9 | 2024 |
| Rlvf: Learning from verbal feedback without overgeneralization M Stephan, A Khazatsky, E Mitchell, AS Chen, S Hsu, A Sharma, C Finn arXiv preprint arXiv:2402.10893, 2024 | 7 | 2024 |
| Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment AS Chen, G Chada, L Smith, A Sharma, Z Fu, S Levine, C Finn arXiv preprint arXiv:2311.01059, 2023 | 6 | 2023 |
| On the opportunities and risks of foundation models [arXiv [cs. LG]]. 2021 R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... | 5 | |
| Calibrating language models with adaptive temperature scaling J Xie, AS Chen, Y Lee, E Mitchell, C Finn arXiv preprint arXiv:2409.19817, 2024 | 3 | 2024 |
| AutoFT: Robust Fine-Tuning by Optimizing Hyperparameters on OOD Data C Choi, Y Lee, A Chen, A Zhou, A Raghunathan, C Finn arXiv preprint arXiv:2401.10220, 2024 | 3 | 2024 |
Chelsea FinnStanford University, Physical IntelligenceVerified email at cs.stanford.edu