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Weishen Pan

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Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs

Oct 25, 2023
Jacqueline Maasch, Weishen Pan, Shantanu Gupta, Volodymyr Kuleshov, Kyra Gan, Fei Wang

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Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustment

Jul 27, 2023
Sen Cui, Weishen Pan, Changshui Zhang, Fei Wang

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InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models

Jun 14, 2023
Yingheng Wang, Yair Schiff, Aaron Gokaslan, Weishen Pan, Fei Wang, Christopher De Sa, Volodymyr Kuleshov

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Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources

May 13, 2023
Suraj Rajendran, Weishen Pan, Mert R. Sabuncu, Yong Chen, Jiayu Zhou, Fei Wang

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Correcting the User Feedback-Loop Bias for Recommendation Systems

Sep 13, 2021
Weishen Pan, Sen Cui, Hongyi Wen, Kun Chen, Changshui Zhang, Fei Wang

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Learning to Collaborate

Aug 19, 2021
Sen Cui, Jian Liang, Weishen Pan, Kun Chen, Changshui Zhang, Fei Wang

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Fair and Consistent Federated Learning

Aug 19, 2021
Sen Cui, Weishen Pan, Jian Liang, Changshui Zhang, Fei Wang

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Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition

Aug 11, 2021
Weishen Pan, Sen Cui, Jiang Bian, Changshui Zhang, Fei Wang

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The Definitions of Interpretability and Learning of Interpretable Models

May 29, 2021
Weishen Pan, Changshui Zhang

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xOrder: A Model Agnostic Post-Processing Framework for Achieving Ranking Fairness While Maintaining Algorithm Utility

Jun 16, 2020
Sen Cui, Weishen Pan, Changshui Zhang, Fei Wang

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