| Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs Y Chen, Y Zhang, Y Bian, H Yang, K Ma, B Xie, T Liu, B Han, J Cheng Advances in Neural Information Processing Systems (NeurIPS 2022), 2022 | 125* | 2022 |
| Understanding and Improving Graph Injection Attack by Promoting Unnoticeability Y Chen, H Yang, Y Zhang, K Ma, T Liu, B Han, J Cheng International Conference on Learning Representations (ICLR 2022), 2022 | 77 | 2022 |
| Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization Y Chen, K Zhou, Y Bian, B Xie, B Wu, Y Zhang, K Ma, H Yang, P Zhao, ... International Conference on Learning Representations (ICLR 2023); Oral …, 2022 | 48 | 2022 |
| Self-enhanced gnn: Improving graph neural networks using model outputs H Yang, X Yan, X Dai, Y Chen, J Cheng IJCNN 2021, 2020 | 38 | 2020 |
| Towards Understanding Feature Learning in Out-of-Distribution Generalization Y Chen*, W Huang*, K Zhou*, Y Bian, B Han, J Cheng Advances in Neural Information Processing Systems (NeurIPS 2023), 2023 | 16* | 2023 |
| Calibrating and Improving Graph Contrastive Learning MA KAILI, Y Garry, H Yang, Y Chen, J Cheng Transactions on Machine Learning Research (TMLR), 2023 | 13* | 2023 |
| Does Invariant Graph Learning via Environment Augmentation Learn Invariance? Y Chen, Y Bian, K Zhou, B Xie, B Han, J Cheng Advances in Neural Information Processing Systems (NeurIPS 2023), 2023 | 11* | 2023 |
| Towards out-of-distribution generalizable predictions of chemical kinetics properties Z Wang*, Y Chen*, Y Duan, W Li, B Han, J Cheng, H Tong Oral presentation at NeurIPS 2023 workshop on AI for Science, 2023 | 5 | 2023 |
| Exact Shape Correspondence via 2D graph convolution BF Kamhoua, L Zhang, Y Chen, H Yang, MA KAILI, B Han, B Li, J Cheng Advances in Neural Information Processing Systems (NeurIPS 2022), 2022 | 4* | 2022 |
| Discovery of the Hidden World with Large Language Models C Liu*, Y Chen*, T Liu, M Gong, J Cheng, B Han, K Zhang arXiv preprint arXiv:2402.03941, 2024 | 2 | 2024 |
| Enhancing Evolving Domain Generalization through Dynamic Latent Representations B Xie, Y Chen, J Wang, K Zhou, B Han, W Meng, J Cheng Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI 2024) Oral …, 2024 | 2 | 2024 |
| Dataset and Baseline System for Multi-lingual Extraction and Normalization of Temporal and Numerical Expressions S Chen, Y Chen, BF Karlsson Microsof Research Technical Report MSR-TR-2023-9, 2023 | 2 | 2023 |
| Do CLIPs Always Generalize Better than ImageNet Models? Q Wang*, Y Lin*, Y Chen*, L Schmidt, B Han, T Zhang arXiv preprint arXiv:2403.11497, 2024 | 1 | 2024 |
| Enhancing Neural Subset Selection: Integrating Background Information into Set Representations B Xie, Y Bian, K Zhou, Y Chen, P Zhao, B Han, W Meng, J Cheng International Conference on Learning Representations (ICLR 2024), 2024 | 1 | 2024 |
| Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes Y Chen, B Xie, K Zhou, B Han, Y Bian, J Cheng arXiv preprint arXiv:2311.18194, 2023 | 1 | 2023 |
| Solving the non-submodular network collapse problems via Decision Transformer K Ma, H Yang, S Yang, K Zhao, L Li, Y Chen, J Huang, J Cheng, Y Rong Neural Networks 176, 106328, 2024 | | 2024 |
| HIGHT: Hierarchical Graph Tokenization for Graph-Language Alignment Y Chen, Q Yao, J Zhang, J Cheng, Y Bian arXiv preprint arXiv:2406.14021, 2024 | | 2024 |
| Empowering Graph Invariance Learning with Deep Spurious Infomax T Yao*, Y Chen*, Z Chen, K Hu, Z Shen, K Zhang International Conference on Machine Learning (ICML 2024), 2024 | | 2024 |
| How Interpretable Are Interpretable Graph Neural Networks? Y Chen, Y Bian, B Han, J Cheng International Conference on Machine Learning (ICML 2024); Spotlight …, 2024 | | 2024 |
Bo HanHKBU / RIKENVerified email at comp.hkbu.edu.hk
