| Impossible tuning made possible: A new expert algorithm and its applications L Chen, H Luo, CY Wei Conference on Learning Theory, 1216-1259, 2021 | 48 | 2021 |
| Minimax regret for stochastic shortest path with adversarial costs and known transition L Chen, H Luo, CY Wei Conference on Learning Theory, 1180-1215, 2021 | 37 | 2021 |
| Learning infinite-horizon average-reward Markov decision process with constraints L Chen, R Jain, H Luo International Conference on Machine Learning, 3246-3270, 2022 | 33 | 2022 |
| Finding the stochastic shortest path with low regret: The adversarial cost and unknown transition case L Chen, H Luo International Conference on Machine Learning, 1651-1660, 2021 | 32 | 2021 |
| Implicit finite-horizon approximation and efficient optimal algorithms for stochastic shortest path L Chen, M Jafarnia-Jahromi, R Jain, H Luo Advances in Neural Information Processing Systems 34, 10849-10861, 2021 | 26 | 2021 |
| Online learning for stochastic shortest path model via posterior sampling M Jafarnia-Jahromi, L Chen, R Jain, H Luo arXiv preprint arXiv:2106.05335, 2021 | 20 | 2021 |
| Follow-the-perturbed-leader for adversarial markov decision processes with bandit feedback Y Dai, H Luo, L Chen Advances in Neural Information Processing Systems 35, 11437-11449, 2022 | 17 | 2022 |
| Improved no-regret algorithms for stochastic shortest path with linear mdp L Chen, R Jain, H Luo International Conference on Machine Learning, 3204-3245, 2022 | 17 | 2022 |
| Hyper-parameter tuning under a budget constraint Z Lu, CK Chiang, F Sha arXiv preprint arXiv:1902.00532, 2019 | 16 | 2019 |
| Policy optimization for stochastic shortest path L Chen, H Luo, A Rosenberg Conference on Learning Theory, 982-1046, 2022 | 14 | 2022 |
| NeurIPS H Hu, Z Xiong, M Qu, X Yuan, MA Côté, Z Liu, J Tang | 12 | 2020 |
| Near-optimal goal-oriented reinforcement learning in non-stationary environments L Chen, H Luo Advances in Neural Information Processing Systems 35, 33973-33984, 2022 | 9 | 2022 |
| Synthesized policies for transfer and adaptation across tasks and environments H Hu, L Chen, B Gong, F Sha Advances in Neural Information Processing Systems 31, 2018 | 9 | 2018 |
| Policy learning and evaluation with randomized quasi-Monte Carlo SMR Arnold, P L'Ecuyer, L Chen, Y Chen, F Sha arXiv preprint arXiv:2202.07808, 2022 | 7 | 2022 |
| Reaching goals is hard: Settling the sample complexity of the stochastic shortest path L Chen, A Tirinzoni, M Pirotta, A Lazaric International Conference on Algorithmic Learning Theory, 310-357, 2023 | 3 | 2023 |
| Posterior sampling-based online learning for the stochastic shortest path model M Jafarnia-Jahromi, L Chen, R Jain, H Luo Uncertainty in Artificial Intelligence, 922-931, 2023 | 2 | 2023 |
| -Coder: Value-Based Deep Reinforcement Learning for Program Synthesis Z Yu, Y Tao, L Chen, T Sun, H Yang arXiv preprint arXiv:2310.03173, 2023 | 1 | 2023 |
| Layered state discovery for incremental autonomous exploration L Chen, A Tirinzoni, A Lazaric, M Pirotta International Conference on Machine Learning, 4953-5001, 2023 | | 2023 |
| Supplementary Material: Synthesize Policies for Transfer and Adaptation across Tasks and Environments H Hu, L Chen, B Gong, F Sha | | |
Haipeng LuoAssociate Professor, University of Southern CaliforniaVerified email at usc.edu
