| 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 |
| 2025 | KDD | Adaptive Conformal Prediction Intervals for Invariant Learning. | Shuxin Liang, Yihan Xiao, Linglong Kong, Wenlu Tang |
| 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 | Tuning-free Estimation and Inference of Cumulative Distribution Function under Local Differential Privacy. | Yi Liu, Qirui Hu, 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 | Opposite Online Learning via Sequentially Integrated Stochastic Gradient Descent Estimators. | Wenhai Cui, Xiaoting Ji, Linglong Kong, Xiaodong Yan |
| 2023 | ICML | Online Local Differential Private Quantile Inference via Self-normalization. | Yi Liu, Qirui Hu, Lei Ding, Linglong Kong |
| 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 |
| 2022 | IJCNN | MTGnet: Multi-Task Spatiotemporal Graph Convolutional Networks for Air Quality Prediction. | Dan Lu, Rui Chen, Shanshan Sui, Qilong Han, Linglong Kong, Yichen Wang |
| 2022 | KDD | TAG: Toward Accurate Social Media Content Tagging with a Concept Graph. | Jiuding Yang, Weidong Guo, Bang Liu, Yakun Yu, Chaoyue Wang, Jinwen Luo, Linglong Kong, Di Niu, Zhen Wen |
| 2021 | CIKM | L2NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning. | Keith G. Mills, Fred X. Han, Mohammad Salameh, Seyed Saeed Changiz Rezaei, Linglong Kong, Wei Lu, Shuo Lian, Shangling Jui, Di Niu |
| 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 |
| 2019 | ICML | Distributional Reinforcement Learning for Efficient Exploration. | Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu |
| 2019 | IJCAI | Ensemble-based Ultrahigh-dimensional Variable Screening. | Wei Tu, Dong Yang, Linglong Kong, Menglu Che, Qian Shi, Guodong Li, Guangjian Tian |
| 2017 | AAAI | Expectile Matrix Factorization for Skewed Data Analysis. | Rui Zhu, Di Niu, Linglong Kong, Zongpeng Li |
| 2017 | CIKM | Growing Story Forest Online from Massive Breaking News. | Bang Liu, Di Niu, Kunfeng Lai, Linglong Kong, Yu Xu |
| 2017 | ICDM | Recover Fine-Grained Spatial Data from Coarse Aggregation. | Bang Liu, Borislav Mavrin, Linglong Kong, Di Niu |
| 2017 | MICCAI | An Unbiased Penalty for Sparse Classification with Application to Neuroimaging Data. | Li Zhang, Dana Cobzas, Alan H. Wilman, Linglong Kong |
| 2016 | ICDM | House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis. | Bang Liu, Borislav Mavrin, Di Niu, Linglong Kong |
| 2010 | MICCAI | Multivariate Varying Coefficient Models for DTI Tract Statistics. | Hongtu Zhu, Martin Styner, Yimei Li, Linglong Kong, Yundi Shi, Weili Lin, Christopher L. Coe, John H. Gilmore |