Convolutional sequence to sequence learning J Gehring, M Auli, D Grangier, D Yarats, YN Dauphin ICML 2017, 2017 | 4143 | 2017 |
Image augmentation is all you need: Regularizing deep reinforcement learning from pixels D Yarats, I Kostrikov, R Fergus ICLR 2021, 2020 | 728* | 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 | 480 | 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 | 404 | 2019 |
Mastering visual continuous control: Improved data-augmented reinforcement learning D Yarats, R Fergus, A Lazaric, L Pinto ICLR 2022, 2021 | 256 | 2021 |
Reinforcement learning with prototypical representations D Yarats, R Fergus, A Lazaric, L Pinto ICML 2021, 2021 | 210 | 2021 |
Automatic data augmentation for generalization in deep reinforcement learning R Raileanu, M Goldstein, D Yarats, I Kostrikov, R Fergus NeurIPS 2021, 2020 | 199* | 2020 |
Generalized inner loop meta-learning E Grefenstette, B Amos, D Yarats, PM Htut, A Molchanov, F Meier, D Kiela, ... arXiv 2019, 2019 | 165 | 2019 |
Quasi-hyperbolic momentum and adam for deep learning J Ma, D Yarats ICLR 2019, 2018 | 162 | 2018 |
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 | 124 | 2021 |
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 | 79 | 2022 |
On the adequacy of untuned warmup for adaptive optimization J Ma, D Yarats AAAI 2021, 2019 | 74 | 2019 |
On the model-based stochastic value gradient for continuous reinforcement learning B Amos, S Stanton, D Yarats, AG Wilson L4DC 2021, 2020 | 67 | 2020 |
Hierarchical decision making by generating and following natural language instructions H Hu, D Yarats, Q Gong, Y Tian, M Lewis NeurIPS 2019, 2019 | 63 | 2019 |
The differentiable cross-entropy method B Amos, D Yarats ICML 2020, 2020 | 60 | 2020 |
Hierarchical text generation and planning for strategic dialogue D Yarats, M Lewis ICML 2018, 2018 | 56 | 2018 |
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 | 52 | 2022 |
Soft actor-critic (sac) implementation in pytorch D Yarats, I Kostrikov https://github.com/denisyarats/pytorch_sac, 2020 | 51 | 2020 |
Watch and match: Supercharging imitation with regularized optimal transport S Haldar, V Mathur, D Yarats, L Pinto Conference on Robot Learning, 32-43, 2023 | 42 | 2023 |
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 | 20 | 2022 |