Kong, Linglong

27 publications

AISTATS 2025 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

ICLR 2025 CBMA: Improving Conformal Prediction Through Bayesian Model Averaging Pankaj Bhagwat, Linglong Kong, Bei Jiang

ICML 2025 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

NeurIPS 2025 Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-Aware Regularized Exploration in Reinforcement Learning Ke Sun, Yingnan Zhao, Enze Shi, Yafei Wang, Xiaodong Yan, Bei Jiang, Linglong Kong

NeurIPS 2025 Understanding Fairness and Prediction Error Through Subspace Decomposition and Influence Analysis Enze Shi, Pankaj Bhagwat, Zhixian Yang, Linglong Kong, Bei Jiang

TMLR 2024 A Distance-Based Anomaly Detection Framework for Deep Reinforcement Learning Hongming Zhang, Ke Sun, Bo Xu, Linglong Kong, Martin Müller

AAAI 2024 Analysis of Differentially Private Synthetic Data: A Measurement Error Approach Yangdi Jiang, Yi Liu, Xiaodong Yan, Anne-Sophie Charest, Linglong Kong, Bei Jiang

NeurIPS 2024 Distributional Reinforcement Learning with Regularized Wasserstein Loss Ke Sun, Yingnan Zhao, Wulong Liu, Bei Jiang, Linglong Kong

ICMLW 2024 How Does Return Distribution in Distributional Reinforcement Learning Help Optimization? Ke Sun, Bei Jiang, Linglong Kong

JMLR 2024 Inference on High-Dimensional Single-Index Models with Streaming Data Dongxiao Han, Jinhan Xie, Jin Liu, Liuquan Sun, Jian Huang, Bei Jiang, Linglong Kong

NeurIPS 2024 Probing Social Bias in Labor Market Text Generation by ChatGPT: A Masked Language Model Approach Lei Ding, Yang Hu, Nicole Denier, Enze Shi, Junxi Zhang, Qirui Hu, Karen D. Hughes, Linglong Kong, Bei Jiang

AAAI 2024 Responsible Bandit Learning via Privacy-Protected Mean-Volatility Utility Shanshan Zhao, Wenhai Cui, Bei Jiang, Linglong Kong, Xiaodong Yan

ICMLW 2024 Reweighted Bellman Targets for Continual Reinforcement Learning Ke Sun, Jun Jin, Xi Chen, Wulong Liu, Linglong Kong

ICML 2024 Sample Average Approximation for Conditional Stochastic Optimization with Dependent Data Yafei Wang, Bo Pan, Mei Li, Jianya Lu, Lingchen Kong, Bei Jiang, Linglong Kong

ICML 2024 Tuning-Free Estimation and Inference of Cumulative Distribution Function Under Local Differential Privacy Yi Liu, Qirui Hu, Linglong Kong

ECML-PKDD 2023 Exploring the Training Robustness of Distributional Reinforcement Learning Against Noisy State Observations Ke Sun, Yingnan Zhao, Shangling Jui, Linglong Kong

NeurIPS 2023 Gaussian Differential Privacy on Riemannian Manifolds Yangdi Jiang, Xiaotian Chang, Yi Liu, Lei Ding, Linglong Kong, Bei Jiang

ICML 2023 Online Local Differential Private Quantile Inference via Self-Normalization Yi Liu, Qirui Hu, Lei Ding, Linglong Kong

AAAI 2023 Opposite Online Learning via Sequentially Integrated Stochastic Gradient Descent Estimators Wenhai Cui, Xiaoting Ji, Linglong Kong, Xiaodong Yan

NeurIPS 2022 Conformalized Fairness via Quantile Regression Meichen Liu, Lei Ding, Dengdeng Yu, Wulong Liu, Linglong Kong, Bei Jiang

NeurIPS 2022 Identification, Amplification and Measurement: A Bridge to Gaussian Differential Privacy Yi Liu, Ke Sun, Bei Jiang, Linglong Kong

AAAI 2022 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

AAAI 2022 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

NeurIPS 2021 Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization Ke Sun, Yafei Wang, Yi Liu, Yingnan Zhao, Bo Pan, Shangling Jui, Bei Jiang, Linglong Kong

ICML 2019 Distributional Reinforcement Learning for Efficient Exploration Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu

IJCAI 2019 Ensemble-Based Ultrahigh-Dimensional Variable Screening Wei Tu, Dong Yang, Linglong Kong, Menglu Che, Qian Shi, Guodong Li, Guangjian Tian

AAAI 2017 Expectile Matrix Factorization for Skewed Data Analysis Rui Zhu, Di Niu, Linglong Kong, Zongpeng Li