| Hierarchical Multi-Scale Gaussian Transformer for Stock Movement Prediction Q Ding, S Wu, H Sun, J Guo, J Guo IJCAI 2020 (Proceedings of the Twenty-Ninth International Joint Conference …, 2020 | 219 | 2020 |
| Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RL R Yang, Y Lu, W Li, H Sun, M Fang, Y Du, X Li, L Han, C Zhang ICLR 2022 (The Tenth International Conference on Learning Representations), 2022 | 75 | 2022 |
| Membership Inference Attacks against Synthetic Data through Overfitting Detection B van Breugel, H Sun, Z Qian, M van der Schaar AISTATS 2023 (The 26th International Conference on Artificial Intelligence …, 2023 | 49 | 2023 |
| Policy Continuation with Hindsight Inverse Dynamics H Sun, Z Li, X Liu, D Lin, B Zhou NeurIPS 2019 (Advances in Neural Information Processing Systems 33), 2019 | 42 | 2019 |
| Adaptive regularization of labels Q Ding, S Wu, H Sun, J Guo, ST Xia arXiv preprint arXiv:1908.05474, 2019 | 40 | 2019 |
| Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL H Sun, A Hüyük, M van der Schaar ICLR 2024 (The Twelfth International Conference on Learning Representations), 2024 | 39* | 2024 |
| Exploit Reward Shifting in Value-Based Deep-RL: Optimistic Curiosity-Based Exploration and Conservative Exploitation via Linear Reward Shaping H Sun, L Han, R Yang, X Ma, J Guo, B Zhou NeurIPS 2022 (Advances in Neural Information Processing Systems) 35, 37719-37734, 2022 | 39* | 2022 |
| Dense reward for free in reinforcement learning from human feedback AJ Chan, H Sun, S Holt, M van der Schaar ICML 2024 (The Forty-first International Conference on Machine Learning), 2024 | 28 | 2024 |
| Reinforcement Learning in the Era of LLMs: What is Essential? What is needed? An RL Perspective on RLHF, Prompting, and Beyond H Sun arXiv preprint arXiv:2310.06147, 2023 | 18 | 2023 |
| Safe exploration by solving early terminated mdp H Sun, Z Xu, M Fang, Z Peng, J Guo, B Dai, B Zhou arXiv preprint arXiv:2107.04200, 2021 | 18* | 2021 |
| Adaptive regularization of labels Q Ding, S Wu, H Sun, J Guo, ST Xia AAAI 2021 (The Thirty-Fifth AAAI Conference on Artificial Intelligence), 2021 | 18* | 2021 |
| Novel policy seeking with constrained optimization H Sun, Z Peng, B Dai, D Lin, B Zhou NeurIPS 2022 Deep RL Workshop, 2022 | 17 | 2022 |
| Supervised Q-Learning can be a Strong Baseline for Continuous Control H Sun, Z Xu, M Fang, B Zhou NeurIPS 2022 Foundation Models for Decision Making Workshop, 2022 | 17* | 2022 |
| Inverse-RLignment: Inverse Reinforcement Learning from Demonstrations for LLM Alignment H Sun, M van der Schaar arXiv preprint arXiv:2405.15624, 2024 | 15* | 2024 |
| Non-local policy optimization via diversity-regularized collaborative exploration Z Peng, H Sun, B Zhou arXiv preprint arXiv:2006.07781, 2020 | 14 | 2020 |
| What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization H Sun, B van Breugel, J Crabbe, N Seedat, M van der Schaar NeurIPS 2023, 2023 | 12 | 2023 |
| Neural Laplace Control for Continuous-time Delayed Systems S Holt, A Hüyük, Z Qian, H Sun, M van der Schaar AISTATS 2023 (The 26th International Conference on Artificial Intelligence …, 2023 | 12 | 2023 |
| Accountability in offline reinforcement learning: Explaining decisions with a corpus of examples H Sun, A Hüyük, D Jarrett, M van der Schaar Advances in Neural Information Processing Systems 36, 2023 | 11* | 2023 |
| On the guaranteed almost equivalence between imitation learning from observation and demonstration Z Cheng, L Liu, A Liu, H Sun, M Fang, D Tao TNNLS (IEEE Transactions on Neural Networks and Learning Systems), 2021 | 9 | 2021 |
| Retrieval-augmented thought process as sequential decision making T Pouplin, H Sun, S Holt, M Van der Schaar arXiv preprint arXiv:2402.07812, 2024 | 7 | 2024 |
Mihaela van der SchaarUniversity of Cambridge, The Alan Turing Institute確認したメール アドレス: ee.ucla.edu