| 2025 | AISTATS | Advancing Fairness in Precision Medicine: A Universal Framework for Optimal Treatment Estimation in Censored Data. | Hongni Wang, Junxi Zhang, Na Li, Linglong Kong, Bei Jiang, Xiaodong Yan |
| 2025 | CIKM | Oblivious Johnson-Lindenstrauss embeddings for compressed Tucker decompositions. | Matthew Pietrosanu, Bei Jiang, Linglong Kong |
| 2025 | ICLR | CBMA: Improving Conformal Prediction through Bayesian Model Averaging. | Pankaj Bhagwat, Linglong Kong, Bei Jiang |
| 2025 | ICML | Differentially Private Analysis for Binary Response Models: Optimality, Estimation, and Inference. | Ce Zhang, Yixin Han, Yafei Wang, Xiaodong Yan, Linglong Kong, Ting Li, Bei Jiang |
| 2024 | AAAI | Analysis of Differentially Private Synthetic Data: A Measurement Error Approach. | Yangdi Jiang, Yi Liu, Xiaodong Yan, Anne-Sophie Charest, Linglong Kong, Bei Jiang |
| 2024 | AAAI | Responsible Bandit Learning via Privacy-Protected Mean-Volatility Utility. | Shanshan Zhao, Wenhai Cui, Bei Jiang, Linglong Kong, Xiaodong Yan |
| 2024 | ICDM | A Bayesian Hierarchical Model for Orthogonal Tucker Decomposition with Oblivious Tensor Compression. | Matthew Pietrosanu, Bei Jiang, Linglong Kong |
| 2024 | ICML | Sample Average Approximation for Conditional Stochastic Optimization with Dependent Data. | Yafei Wang, Bo Pan, Mei Li, Jianya Lu, Lingchen Kong, Bei Jiang, Linglong Kong |
| 2024 | NAACL | Debiasing with Sufficient Projection: A General Theoretical Framework for Vector Representations. | Enze Shi, Lei Ding, Linglong Kong, Bei Jiang |
| 2023 | AAAI | The Sufficiency of Off-Policyness and Soft Clipping: PPO Is Still Insufficient according to an Off-Policy Measure. | Xing Chen, Dongcui Diao, Hechang Chen, Hengshuai Yao, Haiyin Piao, Zhixiao Sun, Zhiwei Yang, Randy Goebel, Bei Jiang, Yi Chang |
| 2023 | SDM | Optimal Smooth Approximation for Quantile Matrix Factorization. | Peng Liu, Yi Liu, Rui Zhu, Linglong Kong, Bei Jiang, Di Niu |
| 2022 | AAAI | Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving. | Lei Ding, Dengdeng Yu, Jinhan Xie, Wenxing Guo, Shenggang Hu, Meichen Liu, Linglong Kong, Hongsheng Dai, Yanchun Bao, Bei Jiang |
| 2022 | AAAI | Sample Average Approximation for Stochastic Optimization with Dependent Data: Performance Guarantees and Tractability. | Yafei Wang, Bo Pan, Wei Tu, Peng Liu, Bei Jiang, Chao Gao, Wei Lu, Shangling Jui, Linglong Kong |
| 2021 | WWW | Meta-HAR: Federated Representation Learning for Human Activity Recognition. | Chenglin Li, Di Niu, Bei Jiang, Xiao Zuo, Jianming Yang |
| 2019 | ICDM | M-estimation in Low-Rank Matrix Factorization: A General Framework. | Wei Tu, Peng Liu, Jingyu Zhao, Yi Liu, Linglong Kong, Guodong Li, Bei Jiang, Guangjian Tian, Hengshuai Yao |
| 2017 | ICONIP | Task-Free Brainprint Recognition Based on Degree of Brain Networks. | Wanzeng Kong, Qiaonan Fan, Luyun Wang, Bei Jiang, Yong Peng, Yanbin Zhang |