| Spectral-based graph convolutional network for directed graphs Y Ma, J Hao, Y Yang, H Li, J Jin, G Chen arXiv preprint arXiv:1907.08990, 2019 | 69 | 2019 |
| A hierarchical reinforcement learning based optimization framework for large-scale dynamic pickup and delivery problems Y Ma, X Hao, J Hao, J Lu, X Liu, T Xialiang, M Yuan, Z Li, J Tang, Z Meng Advances in neural information processing systems 34, 23609-23620, 2021 | 57 | 2021 |
| A multi-graph attributed reinforcement learning based optimization algorithm for large-scale hybrid flow shop scheduling problem F Ni, J Hao, J Lu, X Tong, M Yuan, J Duan, Y Ma, K He Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 42 | 2021 |
| Kogun: accelerating deep reinforcement learning via integrating human suboptimal knowledge P Zhang, J Hao, W Wang, H Tang, Y Ma, Y Duan, Y Zheng Proceedings of 29th International Conference on International Joint …, 2020 | 33 | 2020 |
| Combining sequence and network information to enhance protein–protein interaction prediction L Liu, X Zhu, Y Ma, H Piao, Y Yang, X Hao, Y Fu, L Wang, J Peng BMC bioinformatics 21, 1-13, 2020 | 29 | 2020 |
| Dynamic knapsack optimization towards efficient multi-channel sequential advertising X Hao, Z Peng, Y Ma, G Wang, J Jin, J Hao, S Chen, R Bai, M Xie, M Xu, ... International Conference on Machine Learning, 4060-4070, 2020 | 21 | 2020 |
| Integrating sequence and network information to enhance protein-protein interaction prediction using graph convolutional networks L Liu, Y Ma, X Zhu, Y Yang, X Hao, L Wang, J Peng 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM …, 2019 | 11 | 2019 |
| Machine Learning Enabled Quickly Predicting of Detonation Properties of N‐Containing Molecules for Discovering New Energetic Materials F Hou, Y Ma, Z Hu, S Ding, H Fu, L Wang, X Zhang, G Li Advanced Theory and Simulations 4 (6), 2100057, 2021 | 10 | 2021 |
| Large scale deep reinforcement learning in war-games H Wang, H Tang, J Hao, X Hao, Y Fu, Y Ma 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM …, 2020 | 9 | 2020 |
| ENOTO: Improving Offline-to-Online Reinforcement Learning with Q-Ensembles K Zhao, J Hao, Y Ma, J Liu, Y Zheng, Z Meng Proceedings of the 23rd International Conference on Autonomous Agents and …, 2024 | 8* | 2024 |
| SplitNet: a reinforcement learning based sequence splitting method for the MinMax multiple travelling salesman problem H Liang, Y Ma, Z Cao, T Liu, F Ni, Z Li, J Hao Proceedings of the AAAI Conference on Artificial Intelligence 37 (7), 8720-8727, 2023 | 7 | 2023 |
| Pandr: Fast adaptation to new environments from offline experiences via decoupling policy and environment representations T Sang, H Tang, Y Ma, J Hao, Y Zheng, Z Meng, B Li, Z Wang arXiv preprint arXiv:2204.02877, 2022 | 7 | 2022 |
| Rethinking decision transformer via hierarchical reinforcement learning Y Ma, HAO Jianye, H Liang, C Xiao Forty-first International Conference on Machine Learning, 2023 | 4 | 2023 |
| Uni-RLHF: Universal Platform and Benchmark Suite for Reinforcement Learning with Diverse Human Feedback Y Yuan, J Hao, Y Ma, Z Dong, H Liang, J Liu, Z Feng, K Zhao, Y Zheng The Twelfth International Conference on Learning Representations, 2024 | 2 | 2024 |
| Iteratively refined behavior regularization for offline reinforcement learning X Hu, Y Ma, C Xiao, Y Zheng, HAO Jianye | 2* | 2023 |
| A Trajectory Perspective on the Role of Data Sampling Techniques in Offline Reinforcement Learning J Liu, Y Ma, J Hao, Y Hu, Y Zheng, T Lv, C Fan Proceedings of the 23rd International Conference on Autonomous Agents and …, 2024 | 1 | 2024 |
| Reining generalization in offline reinforcement learning via representation distinction Y Ma, H Tang, D Li, Z Meng Advances in Neural Information Processing Systems 36, 2024 | 1 | 2024 |
| A hierarchical imitation learning-based decision framework for autonomous driving H Liang, Z Dong, Y Ma, X Hao, Y Zheng, J Hao Proceedings of the 32nd ACM International Conference on Information and …, 2023 | 1 | 2023 |
| State-Aware Proximal Pessimistic Algorithms for Offline Reinforcement Learning C Chen, H Tang, Y Ma, C Wang, Q Shen, D Li, J Hao arXiv preprint arXiv:2211.15065, 2022 | 1 | 2022 |
| A Policy-Decoupled Method for High-Quality Data Augmentation in Offline Reinforcement Learning S Lian, Y Ma, J Liu, HAO Jianye, Y Zheng, Z Meng ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems, 0 | 1* | |
Yan Zheng (郑 岩)Tianjin University在 tju.edu.cn 的电子邮件经过验证
