| 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 | 272* | 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 | 130 | 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 | 96 | 2022 |
| 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 | 74 | 2023 |
| 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 | 74* | 2023 |
| Self-enhanced gnn: Improving graph neural networks using model outputs H Yang, X Yan, X Dai, Y Chen, J Cheng IJCNN 2021, 2020 | 47 | 2020 |
| A Sober Look at the Robustness of CLIPs to Spurious Features Q Wang*, Y Lin*, Y Chen*, L Schmidt, B Han, T Zhang Advances in Neural Information Processing Systems (NeurIPS 2024), 2024 | 42* | 2024 |
| Discovery of the Hidden World with Large Language Models C Liu*, Y Chen*, T Liu, M Gong, J Cheng, B Han, K Zhang Advances in Neural Information Processing Systems (NeurIPS 2024), 2024 | 33 | 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 | 25 | 2024 |
| On the Comparison between Multi-modal and Single-modal Contrastive Learning W Huang, A Han, Y Chen, Y Cao, Z Xu, T Suzuki Advances in Neural Information Processing Systems (NeurIPS 2024), 2024 | 20 | 2024 |
| Calibrating and Improving Graph Contrastive Learning K Ma, G YANG, H Yang, Y Chen, J Cheng Transactions on Machine Learning Research (TMLR), 2023 | 19* | 2023 |
| Retrieval-Augmented Generation with Hierarchical Knowledge H Huang, Y Huang, J Yang, Z Pan, Y Chen, K Ma, H Chen, J Cheng Empirical Methods in Natural Language Processing (EMNLP 2025 Findings), 2025 | 18 | 2025 |
| Unimot: Unified molecule-text language model with discrete token representation J Zhang, Y Bian, Y Chen, Q Yao arXiv preprint arXiv:2408.00863, 2024 | 15 | 2024 |
| Enhancing Evolving Domain Generalization through Dynamic Latent Representations B Xie, Y Chen, J Wang, K Zhou, B Han, W Meng, J Cheng Oral presentation at Thirty-Eighth AAAI Conference on Artificial …, 2024 | 14 | 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 | 14 | 2024 |
| HIGHT: Hierarchical graph tokenization for graph-language alignment Y Chen, Q Yao, J Zhang, J Cheng, Y Bian International Conference on Machine Learning (ICML 2025); ICML 2024 Workshop …, 2025 | 10 | 2025 |
| Beyond Pixels: Text Enhances Generalization in Real-World Image Restoration H Sun, W Li, J Liu, K Zhou, Y Chen, Y Guo, Y Li, R Pei, L Peng, Y Yang arXiv preprint arXiv:2412.00878, 2024 | 10 | 2024 |
| 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 workshop on AI for Science, 2023 | 9 | 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 | 7* | 2022 |
| Can Large Language Models Help Experimental Design for Causal Discovery? J Li*, Y Chen*, C Liu, Q Cai, T Liu, B Han, K Zhang, H Xiong Oral presentation at ICML Workshop on Scaling up Intervention Models, 2025 | 4 | 2025 |
Bo HanHKBU / RIKENVerified email at comp.hkbu.edu.hk
