| Learning action representations for reinforcement learning Y Chandak, G Theocharous, J Kostas, S Jordan, P Thomas International conference on machine learning, 941-950, 2019 | 215 | 2019 |
| Evaluating the Performance of Reinforcement Learning Algorithms SM Jordan, Y Chandak, D Cohen, M Zhang, PS Thomas International Conference on Machine Learning, 2020 | 77 | 2020 |
| Towards safe policy improvement for non-stationary MDPs Y Chandak, S Jordan, G Theocharous, M White, PS Thomas Advances in Neural Information Processing Systems 33, 9156-9168, 2020 | 31 | 2020 |
| Learning a better negative sampling policy with deep neural networks for search D Cohen, SM Jordan, WB Croft Proceedings of the 2019 acm sigir international conference on theory of …, 2019 | 17 | 2019 |
| Avoiding model estimation in robust markov decision processes with a generative model W Yang, H Wang, T Kozuno, SM Jordan, Z Zhang arXiv preprint arXiv:2302.01248 4 (11), 2023 | 14 | 2023 |
| Behavior Alignment via Reward Function Optimization D Gupta, Y Chandak, SM Jordan, PS Thomas, B C da Silva Advances in Neural Information Processing Systems 36, 2024 | 12 | 2024 |
| Using Cumulative Distribution Based Performance Analysis to Benchmark Models SM Jordan, D Cohen, PS Thomas NeurIPS 2018 Workshop on Critiquing and Correcting Trends in Machine Learning, 2018 | 11 | 2018 |
| Distributed evaluations: Ending neural point metrics D Cohen, SM Jordan, WB Croft arXiv preprint arXiv:1806.03790, 2018 | 8 | 2018 |
| Robust markov decision processes without model estimation W Yang, H Wang, T Kozuno, SM Jordan, Z Zhang arXiv preprint arXiv:2302.01248, 2023 | 6 | 2023 |
| Position: Benchmarking is limited in reinforcement learning research SM Jordan, A White, BC Da Silva, M White, PS Thomas arXiv preprint arXiv:2406.16241, 2024 | 5 | 2024 |
| Impact of changes in tissue optical properties on near-infrared diffuse correlation spectroscopy measures of skeletal muscle blood flow MF Bartlett, SM Jordan, DM Hueber, MD Nelson Journal of Applied Physiology 130 (4), 1183-1195, 2021 | 5 | 2021 |
| High confidence generalization for reinforcement learning J Kostas, Y Chandak, SM Jordan, G Theocharous, P Thomas International Conference on Machine Learning, 5764-5773, 2021 | 4 | 2021 |
| Goal-space Planning with Subgoal Models C Lo, K Roice, PM Panahi, SM Jordan, G Mihucz, A White, ... arXiv preprint arXiv:2206.02902, 2022 | 3 | 2022 |
| A New View on Planning in Online Reinforcement Learning K Roice, PM Panahi, SM Jordan, A White, M White arXiv preprint arXiv:2406.01562, 2024 | 1 | 2024 |
| From past to future: rethinking eligibility traces D Gupta, SM Jordan, S Chaudhari, B Liu, PS Thomas, BC da Silva Proceedings of the AAAI Conference on Artificial Intelligence 38 (11), 12253 …, 2024 | 1 | 2024 |
| Coagent networks: Generalized and scaled JE Kostas, SM Jordan, Y Chandak, G Theocharous, D Gupta, M White, ... arXiv preprint arXiv:2305.09838, 2023 | 1 | 2023 |
| Learning to use a ratchet by modeling spatial relations in demonstrations LY Ku, S Jordan, J Badger, E Learned-Miller, R Grupen Proceedings of the 2018 International Symposium on Experimental Robotics …, 2020 | 1 | 2020 |
| Rigorous Experimentation For Reinforcement Learning SM Jordan | | 2023 |
| Scientific Experimentation for Reinforcement Learning SM JORDAN | | 2022 |
| Classical Policy Gradient: Preserving Bellman's Principle of Optimality PS Thomas, SM Jordan, Y Chandak, C Nota, J Kostas arXiv preprint arXiv:1906.03063, 2019 | | 2019 |
Philip ThomasUniversity of Massachusetts AmherstVerificeret mail på cs.umass.edu
