| Learning action representations for reinforcement learning Y Chandak, G Theocharous, J Kostas, S Jordan, P Thomas International Conference on Machine Learning, 941-950, 2019 | 188 | 2019 |
| Asynchronous Coagent Networks J Kostas, C Nota, P Thomas International Conference on Machine Learning, 5426-5435, 2020 | 13* | 2020 |
| Structural Credit Assignment in Neural Networks using Reinforcement Learning D Gupta, G Mihucz, M Schlegel, J Kostas, PS Thomas, M White Advances in Neural Information Processing Systems 34, 2021 | 6 | 2021 |
| Seldonian toolkit: Building software with safe and fair machine learning A Hoag, JE Kostas, BC da Silva, PS Thomas, Y Brun 2023 IEEE/ACM 45th International Conference on Software Engineering …, 2023 | 5 | 2023 |
| High Confidence Generalization for Reinforcement Learning J Kostas, Y Chandak, SM Jordan, G Theocharous, P Thomas International Conference on Machine Learning, 5764-5773, 2021 | 3 | 2021 |
| 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 |
| Generating recommendations utilizing an edge-computing-based asynchronous coagent network J Kostas, G Theocharous US Patent App. 17/514,768, 2023 | | 2023 |
| Edge-Compatible Reinforcement Learning for Recommendations JE Kostas, PS Thomas, G Theocharous arXiv preprint arXiv:2112.05812, 2021 | | 2021 |
| 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 Amherst在 cs.umass.edu 的电子邮件经过验证
