| Addressing Function Approximation Error in Actor-Critic Methods S Fujimoto, H van Hoof, D Meger Proceedings of the 35th International Conference on Machine Learning 80 …, 2018 | 5946 | 2018 |
| Off-Policy Deep Reinforcement Learning without Exploration S Fujimoto, D Meger, D Precup Proceedings of the 36th International Conference on Machine Learning 97 …, 2019 | 1628 | 2019 |
| A Minimalist Approach to Offline Reinforcement Learning S Fujimoto, SS Gu Advances in neural information processing systems 34, 20132-20145, 2021 | 733 | 2021 |
| Benchmarking Batch Deep Reinforcement Learning Algorithms S Fujimoto, E Conti, M Ghavamzadeh, J Pineau Deep Reinforcement Learning Workshop NeurIPS 2019, 2019 | 198 | 2019 |
| Horizon: Facebook's Open Source Applied Reinforcement Learning Platform J Gauci, E Conti, Y Liang, K Virochsiri, Y He, Z Kaden, V Narayanan, X Ye, ... Reinforcement Learning for Real Life Workshop in the 36th International …, 2019 | 160 | 2019 |
| GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects EJ Smith, S Fujimoto, A Romero, D Meger Proceedings of the 36th International Conference on Machine Learning 97 …, 2019 | 111 | 2019 |
| Sentiment Analysis: It’s Complicated! K Kenyon-Dean, E Ahmed, S Fujimoto, J Georges-Filteau, C Glasz, ... Proceedings of the 2018 Conference of the North American Chapter of the …, 2018 | 92 | 2018 |
| An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay S Fujimoto, D Meger, D Precup Advances in Neural Information Processing Systems 33, 14219-14230, 2020 | 66 | 2020 |
| Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation E Smith, S Fujimoto, D Meger Advances in Neural Information Processing Systems 31, 6477-6487, 2018 | 56 | 2018 |
| Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error S Fujimoto, D Meger, D Precup, O Nachum, SS Gu Proceedings of the 39th International Conference on Machine Learning 162 …, 2022 | 33 | 2022 |
| For SALE: State-Action Representation Learning for Deep Reinforcement Learning S Fujimoto, WD Chang, E Smith, SS Gu, D Precup, D Meger Advances in Neural Information Processing Systems 36, 2023 | 30 | 2023 |
| A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation S Fujimoto, D Meger, D Precup Proceedings of the 38th International Conference on Machine Learning 139 …, 2021 | 18 | 2021 |
| IL-flOw: Imitation Learning from Observation using Normalizing Flows WD Chang, JCG Higuera, S Fujimoto, D Meger, G Dudek Robot Learning Workshop NeurIPS 2021, 2021 | 12 | 2021 |
| Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank W Zhan, S Fujimoto, Z Zhu, JD Lee, DR Jiang, Y Efroni arXiv preprint arXiv:2410.01101, 2024 | | 2024 |
| Imitation Learning from Observation through Optimal Transport WD Chang, S Fujimoto, D Meger, G Dudek arXiv preprint arXiv:2310.01632, 2023 | | 2023 |
David MegerAssociate Professor at McGill UniversityVerified email at cim.mcgill.ca
