| Learning cut selection for mixed-integer linear programming via hierarchical sequence model Z Wang, X Li, J Wang, Y Kuang, M Yuan, J Zeng, Y Zhang, F Wu The Eleventh International Conference on Learning Representations, 2023 | 51 | 2023 |
| Learning robust policy against disturbance in transition dynamics via state-conservative policy optimization Y Kuang, M Lu, J Wang, Q Zhou, B Li, H Li Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7247-7254, 2022 | 23 | 2022 |
| Promoting stochasticity for expressive policies via a simple and efficient regularization method Q Zhou, Y Kuang, Z Qiu, H Li, J Wang Advances in Neural Information Processing Systems 33, 13504-13514, 2020 | 8 | 2020 |
| Machine learning insides optverse ai solver: Design principles and applications X Li, F Zhu, HL Zhen, W Luo, M Lu, Y Huang, Z Fan, Z Zhou, Y Kuang, ... arXiv preprint arXiv:2401.05960, 2024 | 7 | 2024 |
| State sequences prediction via fourier transform for representation learning M Ye, Y Kuang, J Wang, Y Rui, W Zhou, H Li, F Wu Advances in Neural Information Processing Systems 36, 67565-67588, 2023 | 7 | 2023 |
| Promoting generalization for exact solvers via adversarial instance augmentation H Liu, Y Kuang, J Wang, X Li, Y Zhang, F Wu arXiv preprint arXiv:2310.14161, 2023 | 6 | 2023 |
| Learning to cut via hierarchical sequence/set model for efficient mixed-integer programming J Wang, Z Wang, X Li, Y Kuang, Z Shi, F Zhu, M Yuan, J Zeng, Y Zhang, ... IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024 | 5 | 2024 |
| Rethinking branching on exact combinatorial optimization solver: The first deep symbolic discovery framework Y Kuang, J Wang, H Liu, F Zhu, X Li, J Zeng, HAO Jianye, B Li, F Wu The Twelfth International Conference on Learning Representations, 2024 | 5 | 2024 |
| MILP-StuDio: MILP Instance Generation via Block Structure Decomposition H Liu, J Wang, W Zhang, Z Geng, Y Kuang, X Li, B Li, Y Zhang, F Wu arXiv preprint arXiv:2410.22806, 2024 | 4 | 2024 |
| Robust deep reinforcement learning with adaptive adversarial perturbations in action space Q Liu, Y Kuang, J Wang 2024 International Joint Conference on Neural Networks (IJCNN), 1-8, 2024 | 4 | 2024 |
| Accelerate presolve in large-scale linear programming via reinforcement learning Y Kuang, X Li, J Wang, F Zhu, M Lu, Z Wang, J Zeng, H Li, Y Zhang, F Wu arXiv preprint arXiv:2310.11845, 2023 | 4 | 2023 |
| Towards general algorithm discovery for combinatorial optimization: Learning symbolic branching policy from bipartite graph Y Kuang, J Wang, Y Zhou, X Li, F Zhu, HAO Jianye, F Wu Forty-first International Conference on Machine Learning, 2024 | 3 | 2024 |
| Deep Symbolic Optimization for Combinatorial Optimization: Accelerating Node Selection by Discovering Potential Heuristics H Liu, H Liu, Y Kuang, J Wang, B Li Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2024 | 1 | 2024 |
| Learning robust representation for reinforcement learning with distractions by reward sequence prediction Q Zhou, J Wang, Q Liu, Y Kuang, W Zhou, H Li Uncertainty in Artificial Intelligence, 2551-2562, 2023 | 1 | 2023 |
| Neural Krylov Iteration for Accelerating Linear System Solving J Luo, J Wang, H Wang, Z Geng, H Chen, Y Kuang The Thirty-eighth Annual Conference on Neural Information Processing Systems, 0 | 1 | |
| Long-Term Feature Extraction Via Frequency Prediction for Efficient Reinforcement Learning J Wang, M Ye, Y Kuang, R Yang, W Zhou, H Li, F Wu IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025 | | 2025 |
| LLM4Solver: Large Language Model for Efficient Algorithm Design of Combinatorial Optimization Solver Y Zhou, J Wang, Y Kuang, X Li, W Luo, HAO Jianye, F Wu | | |
