| 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 | 190* | 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 | 107 | 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 | 66 | 2022 |
| 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 | 60* | 2024 |
| Self-enhanced gnn: Improving graph neural networks using model outputs H Yang, X Yan, X Dai, Y Chen, J Cheng IJCNN 2021, 2020 | 43 | 2020 |
| 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 | 41 | 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 | 41* | 2023 |
| Calibrating and Improving Graph Contrastive Learning MA KAILI, Y Garry, H Yang, Y Chen, J Cheng Transactions on Machine Learning Research (TMLR), 2023 | 18* | 2023 |
| 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 | 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 2023 workshop on AI for Science, 2023 | 6 | 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 | 6* | 2022 |
| 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 | 5 | 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 | 5 | 2024 |
| Unimot: Unified molecule-text language model with discrete token representation J Zhang, Y Bian, Y Chen, Q Yao arXiv preprint arXiv:2408.00863, 2024 | 4 | 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 | 4 | 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 | 3 | 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 | 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 |
| 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 |
| 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 arXiv preprint arXiv:2503.01139, 2025 | | 2025 |
Bo HanHKBU / RIKENBestätigte E-Mail-Adresse bei comp.hkbu.edu.hk