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AN ZHANG
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Jahr
Disentangled graph collaborative filtering
X Wang, H Jin, A Zhang, X He, T Xu, TS Chua
Proceedings of the 43rd international ACM SIGIR conference on research and …, 2020
1892020
Discovering invariant rationales for graph neural networks
YX Wu, X Wang, A Zhang, X He, TS Chua
arXiv preprint arXiv:2201.12872, 2022
412022
Towards multi-grained explainability for graph neural networks
X Wang, Y Wu, A Zhang, X He, TS Chua
Advances in Neural Information Processing Systems 34, 18446-18458, 2021
252021
Let invariant rationale discovery inspire graph contrastive learning
S Li, X Wang, A Zhang, Y Wu, X He, TS Chua
International Conference on Machine Learning, 13052-13065, 2022
92022
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
82022
Causal screening to interpret graph neural networks
X Wang, Y Wu, A Zhang, X He, T Chua
42021
A-fmi: Learning attributions from deep networks via feature map importance
A Zhang, X Wang, C Fang, J Shi, T Chua, Z Chen
arXiv preprint arXiv:2104.05527, 2021
32021
CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation
Y Ma, Y He, A Zhang, X Wang, TS Chua
arXiv preprint arXiv:2206.00242, 2022
22022
Deconfounding to explanation evaluation in graph neural networks
X Wang, A Zhang, X Hu, F Feng, X He, TS Chua
arXiv preprint arXiv:2201.08802, 2022
12022
Adversarial Causal Augmentation for Graph Covariate Shift
Y Sui, X Wang, J Wu, A Zhang, X He
arXiv preprint arXiv:2211.02843, 2022
2022
Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering
A Zhang, W Ma, X Wang, TS Chua
arXiv preprint arXiv:2210.11054, 2022
2022
Differentiable Invariant Causal Discovery
Y Wang, A Zhang, X Wang, X He, TS Chua
arXiv preprint arXiv:2205.15638, 2022
2022
Cooperative Explanations of Graph Neural Networks
J Fang, X Wang, A Zhang, Z Liu, X He, TS Chua
2018
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