An Zhang

According to our database, An Zhang authored at least 28 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

General Debiasing for Graph-based Collaborative Filtering via Adversarial Graph Dropout.

An Zhang, Wenchang Ma, Pengbo Wei, Leheng Sheng, Xiang Wang

CoRR, 2024

2023

Reinforced Causal Explainer for Graph Neural Networks.

Xiang Wang, Yingxin Wu, An Zhang, Fuli Feng, Xiangnan He, Tat-Seng Chua

IEEE Trans. Pattern Anal. Mach. Intell., 2023

Towards Goal-oriented Intelligent Tutoring Systems in Online Education.

Yang Deng, Zifeng Ren, An Zhang, Wenqiang Lei, Tat-Seng Chua

CoRR, 2023

Large Language Model Can Interpret Latent Space of Sequential Recommender.

Zhengyi Yang, Jiancan Wu, Yanchen Luo, Jizhi Zhang, Yancheng Yuan, An Zhang, Xiang Wang, Xiangnan He

CoRR, 2023

Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules.

Zhiyuan Liu, Yaorui Shi, An Zhang, Enzhi Zhang, Kenji Kawaguchi, Xiang Wang, Tat-Seng Chua

CoRR, 2023

Robust Collaborative Filtering to Popularity Distribution Shift.

An Zhang, Wenchang Ma, Jingnan Zheng, Xiang Wang, Tat-Seng Chua

CoRR, 2023

On Generative Agents in Recommendation.

An Zhang, Leheng Sheng, Yuxin Chen, Hao Li, Yang Deng, Xiang Wang, Tat-Seng Chua

CoRR, 2023

Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting.

An Zhang, Fangfu Liu, Wenchang Ma, Zhibo Cai, Xiang Wang, Tat-Seng Chua

CoRR, 2023

Invariant Collaborative Filtering to Popularity Distribution Shift.

An Zhang, Jingnan Zheng, Xiang Wang, Yancheng Yuan, Tat-Seng Chua

Proceedings of the ACM Web Conference 2023, 2023

Cooperative Explanations of Graph Neural Networks.

Junfeng Fang, Xiang Wang, An Zhang, Zemin Liu, Xiangnan He, Tat-Seng Chua

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 2023

Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss.

An Zhang, Leheng Sheng, Zhibo Cai, Xiang Wang, Tat-Seng Chua

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules.

Zhiyuan Liu, Yaorui Shi, An Zhang, Enzhi Zhang, Kenji Kawaguchi, Xiang Wang, Tat-Seng Chua

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis.

Junfeng Fang, Wei Liu, Yuan Gao, Zemin Liu, An Zhang, Xiang Wang, Xiangnan He

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Online Distillation-enhanced Multi-modal Transformer for Sequential Recommendation.

Wei Ji, Xiangyan Liu, An Zhang, Yinwei Wei, Yongxin Ni, Xiang Wang

Proceedings of the 31st ACM International Conference on Multimedia, 2023

Redundancy-aware Transformer for Video Question Answering.

Yicong Li, Xun Yang, An Zhang, Chun Feng, Xiang Wang, Tat-Seng Chua

Proceedings of the 31st ACM International Conference on Multimedia, 2023

Discovering Dynamic Causal Space for DAG Structure Learning.

Fangfu Liu, Wenchang Ma, An Zhang, Xiang Wang, Yueqi Duan, Tat-Seng Chua

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Boosting Causal Discovery via Adaptive Sample Reweighting.

An Zhang, Fangfu Liu, Wenchang Ma, Zhibo Cai, Xiang Wang, Tat-Seng Chua

Proceedings of the Eleventh International Conference on Learning Representations, 2023

ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction.

Yaorui Shi, An Zhang, Enzhi Zhang, Zhiyuan Liu, Xiang Wang

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

2022

Adversarial Causal Augmentation for Graph Covariate Shift.

Yongduo Sui, Xiang Wang, Jiancan Wu, An Zhang, Xiangnan He

CoRR, 2022

Differentiable Invariant Causal Discovery.

Yu Wang, An Zhang, Xiang Wang, Xiangnan He, Tat-Seng Chua

CoRR, 2022

Deconfounding to Explanation Evaluation in Graph Neural Networks.

Ying-Xin Wu, Xiang Wang, An Zhang, Xia Hu, Fuli Feng, Xiangnan He, Tat-Seng Chua

CoRR, 2022

Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering.

An Zhang, Wenchang Ma, Xiang Wang, Tat-Seng Chua

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation.

Yunshan Ma, Yingzhi He, An Zhang, Xiang Wang, Tat-Seng Chua

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Let Invariant Rationale Discovery Inspire Graph Contrastive Learning.

Sihang Li, Xiang Wang, An Zhang, Yingxin Wu, Xiangnan He, Tat-Seng Chua

Proceedings of the International Conference on Machine Learning, 2022

Discovering Invariant Rationales for Graph Neural Networks.

Yingxin Wu, Xiang Wang, An Zhang, Xiangnan He, Tat-Seng Chua

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

A-FMI: Learning Attributions from Deep Networks via Feature Map Importance.

An Zhang, Xiang Wang, Chengfang Fang, Jie Shi, Tat-Seng Chua, Zehua Chen

CoRR, 2021

Towards Multi-Grained Explainability for Graph Neural Networks.

Xiang Wang, Ying-Xin Wu, An Zhang, Xiangnan He, Tat-Seng Chua

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Disentangled Graph Collaborative Filtering.

Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, Tat-Seng Chua

Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, 2020