| Learning to remember rare events Ł Kaiser, O Nachum, A Roy, S Bengio International Conference for Learning Representations, 2017 | 231* | 2017 |
| Data-Efficient Hierarchical Reinforcement Learning O Nachum, S Gu, H Lee, S Levine Advances in Neural Information Processing Systems, 2018 | 227 | 2018 |
| Bridging the gap between value and policy based reinforcement learning O Nachum, M Norouzi, K Xu, D Schuurmans Advances in Neural Information Processing Systems, 2775-2785, 2017 | 217 | 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 | 164 | 2018 |
| A Lyapunov-based Approach to Safe Reinforcement Learning Y Chow, O Nachum, E Duenez-Guzman, M Ghavamzadeh Advances in Neural Information Processing Systems, 2018 | 125 | 2018 |
| 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 | 96 | 2018 |
| 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 | 68 | 2018 |
| Near-optimal representation learning for hierarchical reinforcement learning O Nachum, S Gu, H Lee, S Levine arXiv preprint arXiv:1810.01257, 2018 | 57 | 2018 |
| Dualdice: Behavior-agnostic estimation of discounted stationary distribution corrections O Nachum, Y Chow, B Dai, L Li Advances in Neural Information Processing Systems, 2318-2328, 2019 | 52 | 2019 |
| Deepmdp: Learning continuous latent space models for representation learning C Gelada, S Kumar, J Buckman, O Nachum, MG Bellemare arXiv preprint arXiv:1906.02736, 2019 | 42 | 2019 |
| 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 | 42 | 2019 |
| Identifying and correcting label bias in machine learning H Jiang, O Nachum International Conference on Artificial Intelligence and Statistics, 702-712, 2020 | 38 | 2020 |
| Behavior regularized offline reinforcement learning Y Wu, G Tucker, O Nachum arXiv preprint arXiv:1911.11361, 2019 | 32 | 2019 |
| Improving policy gradient by exploring under-appreciated rewards O Nachum, M Norouzi, D Schuurmans International Conference for Learning Representations, 2017 | 26 | 2017 |
| Path consistency learning in tsallis entropy regularized mdps Y Chow, O Nachum, M Ghavamzadeh International Conference on Machine Learning, 979-988, 2018 | 24* | 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 | 22 | 2020 |
| Algaedice: Policy gradient from arbitrary experience O Nachum, B Dai, I Kostrikov, Y Chow, L Li, D Schuurmans arXiv preprint arXiv:1912.02074, 2019 | 17 | 2019 |
| Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning? O Nachum, H Tang, X Lu, S Gu, H Lee, S Levine arXiv preprint arXiv:1909.10618, 2019 | 17 | 2019 |
| Smoothed Action Value Functions for Learning Gaussian Policies O Nachum, M Norouzi, G Tucker, D Schuurmans International Conference on Machine Learning, 2018 | 17 | 2018 |
| Multi-agent manipulation via locomotion using hierarchical sim2real O Nachum, M Ahn, H Ponte, S Gu, V Kumar arXiv preprint arXiv:1908.05224, 2019 | 15 | 2019 |
Dale SchuurmansResearch Scientist, Google Brain / Professor, University of AlbertaVerified email at cs.ualberta.ca
