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Bei Jiang

13 papers at tracked venues · 9 at CORE A* · active 2024–2025

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Venues

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

Frequent coauthors

Ke Sun
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Matthew Pietrosanu
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Enze Shi
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Hongni Wang
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Pankaj Bhagwat
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Ce Zhang
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Yangdi Jiang
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Lei Ding
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Shanshan Zhao
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Yafei Wang
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Papers

  1. Advancing Fairness in Precision Medicine: A Universal Framework for Optimal Treatment Estimation in Censored Data
    AISTATS 2025 ·
    Hongni Wang
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  2. CBMA: Improving Conformal Prediction through Bayesian Model Averaging
    ICLR 2025 ·
    Pankaj Bhagwat
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  3. Differentially Private Analysis for Binary Response Models: Optimality, Estimation, and Inference
    ICML 2025 ·
    Ce Zhang
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  4. Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Regularized Exploration in Reinforcement Learning
    NeurIPS 2025 ·
    Ke Sun
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  5. Oblivious Johnson-Lindenstrauss embeddings for compressed Tucker decompositions
    CIKM 2025 ·
    Matthew Pietrosanu
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  6. Understanding Fairness and Prediction Error through Subspace Decomposition and Influence Analysis
    NeurIPS 2025 ·
    Enze Shi
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  7. A Bayesian Hierarchical Model for Orthogonal Tucker Decomposition with Oblivious Tensor Compression
    ICDM 2024 ·
    Matthew Pietrosanu
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  8. Analysis of Differentially Private Synthetic Data: A Measurement Error Approach
    AAAI 2024 ·
    Yangdi Jiang
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  9. Debiasing with Sufficient Projection: A General Theoretical Framework for Vector Representations
    NAACL 2024 ·
    Enze Shi
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  10. Distributional Reinforcement Learning with Regularized Wasserstein Loss
    NeurIPS 2024 ·
    Ke Sun
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  11. Probing Social Bias in Labor Market Text Generation by ChatGPT: A Masked Language Model Approach
    NeurIPS 2024 ·
    Lei Ding
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  12. Responsible Bandit Learning via Privacy-Protected Mean-Volatility Utility
    AAAI 2024 ·
    Shanshan Zhao
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  13. Sample Average Approximation for Conditional Stochastic Optimization with Dependent Data
    ICML 2024 ·
    Yafei Wang
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