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Linglong Kong

15 papers at tracked venues · 11 at CORE A* · active 2024–2025

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Venues

NeurIPS ×4ICML ×3AAAI ×2AISTATS ×1CIKM ×1ICDM ×1ICLR ×1NAACL ×1SIGKDD ×1

Frequent coauthors

Bei Jiang
×13
Xiaodong Yan
×5
Enze Shi
×4
Yafei Wang
×3
Junxi Zhang
×2
Pankaj Bhagwat
×2
Ke Sun
×2
Yingnan Zhao
×2
Matthew Pietrosanu
×2
Yi Liu
×2
Lei Ding
×2
Qirui Hu
×2

Papers

  1. Adaptive Conformal Prediction Intervals for Invariant Learning
    SIGKDD 2025 ·
    Shuxin Liang
    ,
    Yihan Xiao
    , Linglong Kong,
    Wenlu Tang
  2. Advancing Fairness in Precision Medicine: A Universal Framework for Optimal Treatment Estimation in Censored Data
    AISTATS 2025 ·
    Hongni Wang
    ,
    Junxi Zhang
    ,
    Na Li
    , Linglong Kong,
    Bei Jiang
    ,
    Xiaodong Yan
  3. CBMA: Improving Conformal Prediction through Bayesian Model Averaging
    ICLR 2025 ·
    Pankaj Bhagwat
    , Linglong Kong,
    Bei Jiang
  4. Differentially Private Analysis for Binary Response Models: Optimality, Estimation, and Inference
    ICML 2025 ·
    Ce Zhang
    ,
    Yixin Han
    ,
    Yafei Wang
    ,
    Xiaodong Yan
    , Linglong Kong,
    Ting Li
    ,
    Bei Jiang
  5. Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Regularized Exploration in Reinforcement Learning
    NeurIPS 2025 ·
    Ke Sun
    ,
    Yingnan Zhao
    ,
    Enze Shi
    ,
    Yafei Wang
    ,
    Xiaodong Yan
    ,
    Bei Jiang
    , Linglong Kong
  6. Oblivious Johnson-Lindenstrauss embeddings for compressed Tucker decompositions
    CIKM 2025 ·
    Matthew Pietrosanu
    ,
    Bei Jiang
    , Linglong Kong
  7. Understanding Fairness and Prediction Error through Subspace Decomposition and Influence Analysis
    NeurIPS 2025 ·
    Enze Shi
    ,
    Pankaj Bhagwat
    ,
    Zhixian Yang
    , Linglong Kong,
    Bei Jiang
  8. A Bayesian Hierarchical Model for Orthogonal Tucker Decomposition with Oblivious Tensor Compression
    ICDM 2024 ·
    Matthew Pietrosanu
    ,
    Bei Jiang
    , Linglong Kong
  9. Analysis of Differentially Private Synthetic Data: A Measurement Error Approach
    AAAI 2024 ·
    Yangdi Jiang
    ,
    Yi Liu
    ,
    Xiaodong Yan
    ,
    Anne-Sophie Charest
    , Linglong Kong,
    Bei Jiang
  10. Debiasing with Sufficient Projection: A General Theoretical Framework for Vector Representations
    NAACL 2024 ·
    Enze Shi
    ,
    Lei Ding
    , Linglong Kong,
    Bei Jiang
  11. Distributional Reinforcement Learning with Regularized Wasserstein Loss
    NeurIPS 2024 ·
    Ke Sun
    ,
    Yingnan Zhao
    ,
    Wulong Liu
    ,
    Bei Jiang
    , Linglong Kong
  12. Probing Social Bias in Labor Market Text Generation by ChatGPT: A Masked Language Model Approach
    NeurIPS 2024 ·
    Lei Ding
    ,
    Yang Hu
    ,
    Nicole Denier
    ,
    Enze Shi
    ,
    Junxi Zhang
    ,
    Qirui Hu
    ,
    Karen D. Hughes
    , Linglong Kong,
    Bei Jiang
  13. Responsible Bandit Learning via Privacy-Protected Mean-Volatility Utility
    AAAI 2024 ·
    Shanshan Zhao
    ,
    Wenhai Cui
    ,
    Bei Jiang
    , Linglong Kong,
    Xiaodong Yan
  14. Sample Average Approximation for Conditional Stochastic Optimization with Dependent Data
    ICML 2024 ·
    Yafei Wang
    ,
    Bo Pan
    ,
    Mei Li
    ,
    Jianya Lu
    ,
    Lingchen Kong
    ,
    Bei Jiang
    , Linglong Kong
  15. Tuning-free Estimation and Inference of Cumulative Distribution Function under Local Differential Privacy
    ICML 2024 ·
    Yi Liu
    ,
    Qirui Hu
    , Linglong Kong