| Re-understanding Finite-State Representations of Recurrent Policy Networks MH Danesh, A Koul, A Fern, S Khorram International Conference on Machine Learning, 2388-2397, 2021 | 25 | 2021 |
| Out-of-Distribution Dynamics Detection: RL-Relevant Benchmarks and Results MH Danesh, A Fern ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning, 2021 | 17 | 2021 |
| LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty MH Danesh, P Cai, D Hsu https://arxiv.org/abs/2209.11422, 2022 | 11 | 2022 |
| Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning S Huang, Q Gallouédec, F Felten, A Raffin, RFJ Dossa, Y Zhao, ... arXiv preprint arXiv:2402.03046, 2024 | 9 | 2024 |
| Stochastic block-admm for training deep networks S Khorram, X Fu, MH Danesh, Z Qi, L Fuxin arXiv preprint arXiv:2105.00339, 2021 | 3 | 2021 |
| Learning finite state representations of recurrent policy networks MH Danesh, A Koul, A Fern, S Khorram Intl. Conference on Learning Representaitons, 2019 | 2 | 2019 |
| Getting By Goal Misgeneralization With a Little Help From a Mentor T Trinh, MH Danesh, NX Khanh, B Plaut arXiv preprint arXiv:2410.21052, 2024 | 1 | 2024 |
| Taming the Tail in Class-Conditional GANs: Knowledge Sharing via Unconditional Training at Lower Resolutions S Khorram, M Jiang, M Shahbazi, MH Danesh, L Fuxin Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 1 | 2024 |
| Enhancing Transfer of Reinforcement Learning Agents with Abstract Contextual Embeddings G Azran, MH Danesh, SV Albrecht, S Keren NeurIPS 2022 Workshop on Neuro Causal and Symbolic AI (nCSI), 2022 | 1 | 2022 |
| Learning to Coordinate with Experts MH Danesh, T Trinh, B Plaut, NX Khanh arXiv preprint arXiv:2502.09583, 2025 | | 2025 |
| Contextual pre-planning on reward machine abstractions for enhanced transfer in deep reinforcement learning G Azran, MH Danesh, SV Albrecht, S Keren Proceedings of the AAAI Conference on Artificial Intelligence 38 (10), 10953 …, 2024 | | 2024 |
| Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning S Huang, Q Gallouédec, F Felten, A Raffin, RFJ Dossa, Y Zhao, ... https://github.com/openrlbenchmark/openrlbenchmark, 2024 | | 2024 |
| Reducing Neural Network Parameter Initialization Into an SMT Problem (Student Abstract) MH Danesh Proceedings of the AAAI Conference on Artificial Intelligence 35 (18), 15775 …, 2021 | | 2021 |
| Autonomous Agents Research Group S Albrecht, F Christianos, L Schäfer, T McInroe, M Dunion, A Jelley, ... | | 2020 |
| Tracking Moving Objects in Multi-Camera Environments using Appearance Features MH Danesh, M Hasheminejad, A Nickabadi 2018 International Computer Conference (CSICC), 2018 | | 2018 |
| Mitigating Distribution Shifts: Uncertainty-Aware Offline-to-Online Reinforcement Learning MH Danesh, M Wabartha, J Pineau, HC Lin | | |
| Automatic Environment Generation to Generalize Agents MH Danesh | | |
Saeed KhorramApple; Oregon State UniversityZweryfikowany adres z apple.com