| Learning How to Propagate Messages in Graph Neural Networks T Xiao, Z Chen, D Wang, S Wang ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021 | 85 | 2021 |
| A General Offline Reinforcement Learning Framework for Interactive Learning T Xiao, D Wang The Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021 | 84* | 2021 |
| Neural Variational Hybrid Collaborative Filtering T Xiao, S Liang, H Shen, Z Meng arXiv preprint, 2019 | 72* | 2019 |
| BA-GNN: On Learning Bias-aware Graph Neural Network Z Chen, T Xiao*, K Kuang International Conference on Data Engineering (ICDE), 3012-3024, 2022 | 54 | 2022 |
| Decoupled Self-supervised Learning for Graphs T Xiao, Z Chen, Z Guo, Z Zhuang, S Wang NeurIPS 2022, 2022 | 51 | 2022 |
| ISLF: Interest Shift and Latent Factors Combination Model for Session-based Recommendation. J Song, H Shen, Z Ou, J Zhang, T Xiao, S Liang International Joint Conferences on Artificial Intelligence, 2019 | 48 | 2019 |
| Simple and Asymmetric Graph Contrastive Learning without Augmentations T Xiao, H Zhu, Z Chen, S Wang NeurIPS 2023, 2023 | 35 | 2023 |
| Semi-supervisedly Co-embedding Attributed Networks Z Meng, S Liang, J Fang, T Xiao NeurIPS 2019, 2019 | 35 | 2019 |
| Hierarchical Neural Variational Model for Personalized Sequential Recommendation T Xiao, S Liang, Z Meng The World Wide Web Conference (WWW), 2019 | 35 | 2019 |
| Towards Unbiased and Robust Causal Ranking for Recommender Systems T Xiao, S Wang ACM International Conference on Web Search and Data Mining, 2022 | 25 | 2022 |
| Dynamic Bayesian Metric Learning for Personalized Product Search T Xiao, J Ren, Z Meng, H Sun, S Liang ACM International Conference on Information and Knowledge Management, 2019 | 25 | 2019 |
| Bayesian Deep Collaborative Matrix Factorization T Xiao, S Liang, W Shen, Z Meng The Thirty-Third AAAI Conference on Artificial Intelligence, 2019 | 24 | 2019 |
| Towards Fair Graph Neural Networks via Graph Counterfactual Z Guo, J Li, T Xiao, Y Ma, S Wang ACM International Conference on Information and Knowledge Management, 2023 | 22 | 2023 |
| Learning to Reweight for Graph Neural Network Z Chen, T Xiao, K Kuang, Z Lv, M Zhang, J Yang, C Lu, H Yang, F Wu arXiv preprint arXiv:2312.12475, 2023 | 20* | 2023 |
| Counterfactual Learning on Graphs: A Survey Z Guo, T Xiao, Z Wu, C Aggarwal, H Liu, S Wang arXiv preprint arXiv:2304.01391, 2023 | 20 | 2023 |
| Certifiably Robust Graph Contrastive Learning M Lin, T Xiao, E Dai, X Zhang, S Wang NeurIPS 2023, 2023 | 17 | 2023 |
| In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation S Chen, M Xiong, J Liu, Z Wu, T Xiao, S Gao, J He ICML 2024, 2024 | 12 | 2024 |
| Representation Matters When Learning from Biased Feedback in Recommendation T Xiao, Z Chen, S Wang ACM International Conference on Information & Knowledge Management, 2022 | 12 | 2022 |
| 3M-Diffusion: Latent Multi-Modal Diffusion for Text-Guided Generation of Molecular Graphs H Zhu, T Xiao*, VG Honavar COLM 2024, 2024 | 11* | 2024 |
| Decoupled self-supervised learning for non-homophilous graphs T Xiao, Z Chen, Z Guo, Z Zhuang, S Wang arXiv preprint arXiv:2206.03601, 2022 | 11 | 2022 |