| Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system T Wei, F Feng, J Chen, Z Wu, J Yi, X He Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 231 | 2021 |
| Fast adaptation for cold-start collaborative filtering with meta-learning T Wei, Z Wu, R Li, Z Hu, F Feng, X He, Y Sun, W Wang 2020 IEEE International Conference on Data Mining (ICDM), 661-670, 2020 | 58 | 2020 |
| Deep active learning by leveraging training dynamics H Wang, W Huang, Z Wu, H Tong, AJ Margenot, J He Advances in Neural Information Processing Systems 35, 25171-25184, 2022 | 22 | 2022 |
| Fairness-aware model-agnostic positive and unlabeled learning Z Wu, J He Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 11 | 2022 |
| Training fair deep neural networks by balancing influence H Wang, Z Wu, J He arXiv preprint arXiv:2201.05759, 2022 | 10 | 2022 |
| Neural Active Learning Beyond Bandits Y Ban, I Agarwal, Z Wu, Y Zhu, K Weldemariam, H Tong, J He arXiv preprint arXiv:2404.12522, 2024 | 1 | 2024 |
| FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes H Wang, Z Wu, J He Proceedings of the 17th ACM International Conference on Web Search and Data …, 2024 | | 2024 |
Tianxin WeiUniversity of Illinois Urbana ChampaignVerified email at illinois.edu
