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Learning to Counterfactually Explain Recommendations


Nov 17, 2022
Yuanshun Yao, Chong Wang, Hang Li


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Evaluating Fairness Without Sensitive Attributes: A Framework Using Only Auxiliary Models


Oct 06, 2022
Zhaowei Zhu, Yuanshun Yao, Jiankai Sun, Yang Liu, Hang Li


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DPAUC: Differentially Private AUC Computation in Federated Learning


Aug 25, 2022
Jiankai Sun, Xin Yang, Yuanshun Yao, Junyuan Xie, Di Wu, Chong Wang


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Differentially Private Multi-Party Data Release for Linear Regression


Jun 18, 2022
Ruihan Wu, Xin Yang, Yuanshun Yao, Jiankai Sun, Tianyi Liu, Kilian Q. Weinberger, Chong Wang

* UAI 2022 

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Differentially Private AUC Computation in Vertical Federated Learning


May 24, 2022
Jiankai Sun, Xin Yang, Yuanshun Yao, Junyuan Xie, Di Wu, Chong Wang


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Label Leakage and Protection from Forward Embedding in Vertical Federated Learning


Mar 04, 2022
Jiankai Sun, Xin Yang, Yuanshun Yao, Chong Wang


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Differentially Private Label Protection in Split Learning


Mar 04, 2022
Xin Yang, Jiankai Sun, Yuanshun Yao, Junyuan Xie, Chong Wang


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Counterfactually Evaluating Explanations in Recommender Systems


Mar 02, 2022
Yuanshun Yao, Chong Wang, Hang Li


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Defending against Reconstruction Attack in Vertical Federated Learning


Jul 21, 2021
Jiankai Sun, Yuanshun Yao, Weihao Gao, Junyuan Xie, Chong Wang

* Accepted to International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2021 (FL-ICML'21) 

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Vertical Federated Learning without Revealing Intersection Membership


Jun 10, 2021
Jiankai Sun, Xin Yang, Yuanshun Yao, Aonan Zhang, Weihao Gao, Junyuan Xie, Chong Wang


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