| Discovering invariant rationales for graph neural networks YX Wu, X Wang, A Zhang, X He, TS Chua ICLR 2022, 2022 | 180 | 2022 |
| Let invariant rationale discovery inspire graph contrastive learning S Li, X Wang, A Zhang, Y Wu, X He, TS Chua ICML 2022, 2022 | 81 | 2022 |
| Towards multi-grained explainability for graph neural networks X Wang, Y Wu, A Zhang, X He, TS Chua NeurIPS 2021, 2021 | 69 | 2021 |
| Med-flamingo: a multimodal medical few-shot learner M Moor, Q Huang, S Wu, M Yasunaga, Y Dalmia, J Leskovec, C Zakka, ... Machine Learning for Health (ML4H), 353-367, 2023 | 55 | 2023 |
| Reinforced causal explainer for graph neural networks X Wang, Y Wu, A Zhang, F Feng, X He, TS Chua IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 | 37 | 2022 |
| Discover and Cure: Concept-aware Mitigation of Spurious Correlation S Wu, M Yuksekgonul, L Zhang, J Zou ICML 2023, 2023 | 28 | 2023 |
| Holistic analysis of hallucination in gpt-4v (ision): Bias and interference challenges C Cui, Y Zhou, X Yang, S Wu, L Zhang, J Zou, H Yao arXiv preprint arXiv:2311.03287, 2023 | 26 | 2023 |
| Deconfounding to explanation evaluation in graph neural networks YX Wu, X Wang, A Zhang, X Hu, F Feng, X He, TS Chua arXiv preprint arXiv:2201.08802, 2022 | 13 | 2022 |
| D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion J Chen, S Wu, A Gupta, R Ying NeurIPS 2023, 2023 | 3 | 2023 |
| GraphMETRO: Mitigating Complex Distribution Shifts in GNNs via Mixture of Aligned Experts S Wu, K Cao, B Ribeiro, J Zou, J Leskovec arXiv preprint arXiv:2312.04693, 2023 | 1 | 2023 |
James ZouStanford UniversityVerified email at stanford.edu
