| Automatic data augmentation for generalization in reinforcement learning R Raileanu, M Goldstein, D Yarats, I Kostrikov, R Fergus Advances in Neural Information Processing Systems 34, 5402-5415, 2021 | 189* | 2021 |
| Learning generalized reactive policies using deep neural networks E Groshev, M Goldstein, A Tamar, S Srivastava, P Abbeel Proceedings of the International Conference on Automated Planning and …, 2018 | 126 | 2018 |
| Fast adaptation to new environments via policy-dynamics value functions R Raileanu, M Goldstein, A Szlam, R Fergus Proceedings of the 37th International Conference on Machine Learning, 7920-7931, 2020 | 34* | 2020 |
| PAC-Bayes Control: synthesizing controllers that provably generalize to novel environments A Majumdar, M Goldstein Conference on robot learning, 293-305, 2018 | 21 | 2018 |
| A non-rigid point and normal registration algorithm with applications to learning from demonstrations AX Lee, MA Goldstein, ST Barratt, P Abbeel 2015 IEEE International Conference on Robotics and Automation (ICRA), 935-942, 2015 | 14 | 2015 |
| Converging to Unexploitable Policies in Continuous Control Adversarial Games M Goldstein, N Brown Deep Reinforcement Learning Workshop NeurIPS 2022, 2022 | | 2022 |
| Match Prediction Using Learned History Embeddings M Goldstein, L Bottou, R Fergus | | 2021 |
