| Convolutional sequence to sequence learning J Gehring, M Auli, D Grangier, D Yarats, YN Dauphin ICML 2017, 2017 | 4819 | 2017 |
| Image augmentation is all you need: Regularizing deep reinforcement learning from pixels D Yarats, I Kostrikov, R Fergus ICLR 2021, 2020 | 1086* | 2020 |
| Deal or no deal? end-to-end learning for negotiation dialogues M Lewis, D Yarats, YN Dauphin, D Parikh, D Batra EMNLP 2017, 2017 | 624 | 2017 |
| Improving sample efficiency in model-free reinforcement learning from images D Yarats, A Zhang, I Kostrikov, B Amos, J Pineau, R Fergus AAAI 2021, 2019 | 585 | 2019 |
| Mastering visual continuous control: Improved data-augmented reinforcement learning D Yarats, R Fergus, A Lazaric, L Pinto ICLR 2022, 2021 | 499 | 2021 |
| Reinforcement learning with prototypical representations D Yarats, R Fergus, A Lazaric, L Pinto ICML 2021, 2021 | 311 | 2021 |
| Automatic data augmentation for generalization in deep reinforcement learning R Raileanu, M Goldstein, D Yarats, I Kostrikov, R Fergus NeurIPS 2021, 2020 | 303* | 2020 |
| URLB: Unsupervised Reinforcement Learning Benchmark M Laskin, D Yarats, H Liu, K Lee, A Zhan, K Lu, C Cang, L Pinto, P Abbeel NeurIPS 2021, 2021 | 217 | 2021 |
| Quasi-hyperbolic momentum and adam for deep learning J Ma, D Yarats ICLR 2019, 2018 | 203 | 2018 |
| Generalized inner loop meta-learning E Grefenstette, B Amos, D Yarats, PM Htut, A Molchanov, F Meier, D Kiela, ... arXiv 2019, 2019 | 202 | 2019 |
| Don't change the algorithm, change the data: Exploratory data for offline reinforcement learning D Yarats, D Brandfonbrener, H Liu, M Laskin, P Abbeel, A Lazaric, L Pinto arXiv preprint arXiv:2201.13425, 2022 | 141 | 2022 |
| Watch and match: Supercharging imitation with regularized optimal transport S Haldar, V Mathur, D Yarats, L Pinto Conference on Robot Learning, 32-43, 2023 | 109 | 2023 |
| On the adequacy of untuned warmup for adaptive optimization J Ma, D Yarats AAAI 2021, 2019 | 104 | 2019 |
| On the model-based stochastic value gradient for continuous reinforcement learning B Amos, S Stanton, D Yarats, AG Wilson L4DC 2021, 2020 | 90 | 2020 |
| Cic: Contrastive intrinsic control for unsupervised skill discovery M Laskin, H Liu, XB Peng, D Yarats, A Rajeswaran, P Abbeel arXiv preprint arXiv:2202.00161, 2022 | 85 | 2022 |
| Hierarchical decision making by generating and following natural language instructions H Hu, D Yarats, Q Gong, Y Tian, M Lewis NeurIPS 2019, 2019 | 77 | 2019 |
| Hierarchical text generation and planning for strategic dialogue D Yarats, M Lewis ICML 2018, 2018 | 76 | 2018 |
| The differentiable cross-entropy method B Amos, D Yarats ICML 2020, 2020 | 74 | 2020 |
| Soft actor-critic (sac) implementation in pytorch D Yarats, I Kostrikov https://github.com/denisyarats/pytorch_sac, 2020 | 66 | 2020 |
| Unsupervised reinforcement learning with contrastive intrinsic control M Laskin, H Liu, XB Peng, D Yarats, A Rajeswaran, P Abbeel Advances in Neural Information Processing Systems 35, 34478-34491, 2022 | 59 | 2022 |
Rob FergusProfessor of Computer Science, New York UniversityVerified email at cs.nyu.edu
