| Model-agnostic meta-learning for fast adaptation of deep networks C Finn, P Abbeel, S Levine International Conference on Machine Learning (ICML), 1126-1135, 2017 | 7624 | 2017 |
| End-to-end training of deep visuomotor policies S Levine, C Finn, T Darrell, P Abbeel Journal of Machine Learning Research 17 (1), 1334-1373, 2016 | 3142 | 2016 |
| Unsupervised learning for physical interaction through video prediction C Finn, I Goodfellow, S Levine Advances in neural information processing systems 29, 2016 | 981 | 2016 |
| Guided cost learning: Deep inverse optimal control via policy optimization C Finn, S Levine, P Abbeel International Conference on Machine Learning (ICML), 49-58, 2016 | 833 | 2016 |
| Deep visual foresight for planning robot motion C Finn, S Levine 2017 IEEE International Conference on Robotics and Automation (ICRA), 2786-2793, 2017 | 654 | 2017 |
| Probabilistic model-agnostic meta-learning C Finn, K Xu, S Levine Neural Information Processing Systems (NeurIPS), 2018 | 596 | 2018 |
| Deep spatial autoencoders for visuomotor learning C Finn, XY Tan, Y Duan, T Darrell, S Levine, P Abbeel 2016 IEEE International Conference on Robotics and Automation (ICRA), 512-519, 2016 | 575* | 2016 |
| Model-based reinforcement learning for atari L Kaiser, M Babaeizadeh, P Milos, B Osinski, RH Campbell, ... International Conference on Learning Representations (ICLR), 2020 | 570 | 2020 |
| Meta-learning with implicit gradients A Rajeswaran, C Finn, S Kakade, S Levine Neural Information Processing Systems (NeurIPS), 2019 | 483 | 2019 |
| Wilds: A benchmark of in-the-wild distribution shifts PW Koh, S Sagawa, H Marklund, SM Xie, M Zhang, A Balsubramani, ... International Conference on Machine Learning (ICML), 5637-5664, 2021 | 463 | 2021 |
| One-shot visual imitation learning via meta-learning C Finn, T Yu, T Zhang, P Abbeel, S Levine Conference on Robot Learning (CoRL), 2017 | 459 | 2017 |
| Learning to adapt in dynamic, real-world environments through meta-reinforcement learning A Nagabandi, I Clavera, S Liu, RS Fearing, P Abbeel, S Levine, C Finn International Conference on Learning Representations (ICLR), 2019 | 458* | 2019 |
| Recasting gradient-based meta-learning as hierarchical bayes E Grant, C Finn, S Levine, T Darrell, T Griffiths International Conference on Learning Representations (ICLR), 2018 | 452 | 2018 |
| 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 | 445 | 2021 |
| Stochastic variational video prediction M Babaeizadeh, C Finn, D Erhan, RH Campbell, S Levine International Conference on Learning Representations (ICLR), 2017 | 435 | 2017 |
| Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning T Yu, D Quillen, Z He, R Julian, K Hausman, C Finn, S Levine Conference on Robot Learning (CoRL), 2019 | 417 | 2019 |
| Efficient off-policy meta-reinforcement learning via probabilistic context variables K Rakelly, A Zhou, D Quillen, C Finn, S Levine International Conference on Machine Learning (ICML), 2019 | 397 | 2019 |
| Stochastic adversarial video prediction AX Lee, R Zhang, F Ebert, P Abbeel, C Finn, S Levine arXiv preprint arXiv:1804.01523, 2018 | 366 | 2018 |
| Gradient surgery for multi-task learning T Yu, S Kumar, A Gupta, S Levine, K Hausman, C Finn Neural Information Processing Systems (NeurIPS), 2020 | 325 | 2020 |
| Online meta-learning C Finn, A Rajeswaran, S Kakade, S Levine International Conference on Machine Learning (ICML), 2019 | 325 | 2019 |
Sergey LevineUC Berkeley, GoogleVerified email at eecs.berkeley.edu
