| LinSATNet: the positive linear satisfiability neural networks R Wang, Y Zhang, Z Guo, T Chen, X Yang, J Yan International Conference on Machine Learning, 36605-36625, 2023 | 8 | 2023 |
| Deep learning of partial graph matching via differentiable top-k R Wang, Z Guo, S Jiang, X Yang, J Yan Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 7 | 2023 |
| Pygmtools: A python graph matching toolkit R Wang, Z Guo, W Pan, J Ma, Y Zhang, N Yang, Q Liu, L Wei, H Zhang, ... Journal of Machine Learning Research 25, 1-7, 2024 | 4 | 2024 |
| Towards imitation learning to branch for mip: A hybrid reinforcement learning based sample augmentation approach C Zhang, W Ouyang, H Yuan, L Gong, Y Sun, Z Guo, Z Dong, J Yan The Twelfth International Conference on Learning Representations, 2024 | 4 | 2024 |
| ACM-MILP: Adaptive Constraint Modification via Grouping and Selection for Hardness-Preserving MILP Instance Generation Z Guo, Y Li, C Liu, W Ouyang, J Yan Forty-first International Conference on Machine Learning, 0 | 1 | |
| GlobalWalk: Learning Global-aware Node Embeddings via Biased Sampling Z Xue, Z Guo, Y Guo arXiv preprint arXiv:2201.09882, 2022 | | 2022 |
| BTBS-LNS: A Binarized-Tightening, Branch and Search Approach of Learning Large Neighborhood Search Policies for MIP H Yuan, W Ouyang, C Zhang, Y Sun, L Gong, Z Guo, Z Dong, J Yan | | |
