| Data-Efficient Hierarchical Reinforcement Learning O Nachum, S Gu, H Lee, S Levine Advances in Neural Information Processing Systems, 2018 | 687 | 2018 |
| D4rl: Datasets for deep data-driven reinforcement learning J Fu, A Kumar, O Nachum, G Tucker, S Levine arXiv preprint arXiv:2004.07219, 2020 | 498 | 2020 |
| A Lyapunov-based Approach to Safe Reinforcement Learning Y Chow, O Nachum, E Duenez-Guzman, M Ghavamzadeh Advances in Neural Information Processing Systems, 2018 | 417 | 2018 |
| Behavior regularized offline reinforcement learning Y Wu, G Tucker, O Nachum arXiv preprint arXiv:1911.11361, 2019 | 415 | 2019 |
| Bridging the gap between value and policy based reinforcement learning O Nachum, M Norouzi, K Xu, D Schuurmans Advances in neural information processing systems 30, 2017 | 399 | 2017 |
| Learning to remember rare events Ł Kaiser, O Nachum, A Roy, S Bengio International Conference for Learning Representations, 2017 | 375 | 2017 |
| Morphnet: Fast & simple resource-constrained structure learning of deep networks A Gordon, E Eban, O Nachum, B Chen, H Wu, TJ Yang, E Choi Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 336 | 2018 |
| Dualdice: Behavior-agnostic estimation of discounted stationary distribution corrections O Nachum, Y Chow, B Dai, L Li Advances in Neural Information Processing Systems 32, 2019 | 234 | 2019 |
| Deepmdp: Learning continuous latent space models for representation learning C Gelada, S Kumar, J Buckman, O Nachum, MG Bellemare International Conference on Machine Learning, 2170-2179, 2019 | 222 | 2019 |
| Identifying and correcting label bias in machine learning H Jiang, O Nachum International Conference on Artificial Intelligence and Statistics, 702-712, 2020 | 211 | 2020 |
| Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods D Quillen, E Jang, O Nachum, C Finn, J Ibarz, S Levine IEEE International Conference on Robotics and Automation, 2018 | 209 | 2018 |
| Lyapunov-based safe policy optimization for continuous control Y Chow, O Nachum, A Faust, E Duenez-Guzman, M Ghavamzadeh arXiv preprint arXiv:1901.10031, 2019 | 183 | 2019 |
| Near-optimal representation learning for hierarchical reinforcement learning O Nachum, S Gu, H Lee, S Levine arXiv preprint arXiv:1810.01257, 2018 | 175 | 2018 |
| Algaedice: Policy gradient from arbitrary experience O Nachum, B Dai, I Kostrikov, Y Chow, L Li, D Schuurmans arXiv preprint arXiv:1912.02074, 2019 | 153 | 2019 |
| Offline reinforcement learning with fisher divergence critic regularization I Kostrikov, R Fergus, J Tompson, O Nachum International Conference on Machine Learning, 5774-5783, 2021 | 147 | 2021 |
| Rl unplugged: A suite of benchmarks for offline reinforcement learning C Gulcehre, Z Wang, A Novikov, T Paine, S Gómez, K Zolna, R Agarwal, ... Advances in Neural Information Processing Systems 33, 7248-7259, 2020 | 112* | 2020 |
| Trust-pcl: An off-policy trust region method for continuous control O Nachum, M Norouzi, K Xu, D Schuurmans International Conference for Learning Representations, 2018 | 112 | 2018 |
| Imitation learning via off-policy distribution matching I Kostrikov, O Nachum, J Tompson arXiv preprint arXiv:1912.05032, 2019 | 110 | 2019 |
| Opal: Offline primitive discovery for accelerating offline reinforcement learning A Ajay, A Kumar, P Agrawal, S Levine, O Nachum arXiv preprint arXiv:2010.13611, 2020 | 99 | 2020 |
| Deployment-efficient reinforcement learning via model-based offline optimization T Matsushima, H Furuta, Y Matsuo, O Nachum, S Gu arXiv preprint arXiv:2006.03647, 2020 | 97 | 2020 |
Dale SchuurmansResearch Scientist, Google Brain / Professor, University of AlbertaVerified email at cs.ualberta.ca
