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Papers from SAIL

  • Yongkai Wu and Xintao Wu, Using Loglinear Model for Discrimination Discovery and Prevention, in DSAA 2016, link
  • Lu Zhang, Yongkai Wu, and Xintao Wu, Situation Testing-Based Discrimination Discovery: A Causa Inference Approach, in IJCAI 2016, link
  • Lu Zhang, Yongkai Wu, and Xintao Wu, On Discrimination Discovery Using Causal Networks, in SBP-BRiMS 2016, link
  • Lu Zhang, Yongkai Wu, and Xintao Wu, A Causal Framework for Discovering and Removing Direct an Indirect Discrimination, in IJCAI 2017, link
  • Lu Zhang, Yongkai Wu, and Xintao Wu, Achieving Non-Discrimination in Data Release, in KDD 2017, link
  • Lu Zhang and Xintao Wu, Anti-discrimination learning: a causal modeling-based framework, in JDSA, link
  • Yongkai Wu, Lu Zhang, and Xintao Wu, On Discrimination Discovery and Removal in Ranked Data using Causal Graph, in KDD 2018, link
  • Lu Zhang, Yongkai Wu, and Xintao Wu, Achieving Non-Discrimination in Prediction, in IJCAI 2018, link
  • Lu Zhang, Yongkai Wu, and Xintao Wu, Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms, in TKDE, link
  • Depeng Xu, Shuhan Yuan, Lu Zhang, and Xintao Wu, FairGAN: Fairness-aware Generative Adversarial Networks, in IEEE Big Data 2018, link
  • Yongkai Wu, Lu Zhang, and Xintao Wu, On Convexity and Bounds of Fairness-aware Classification, in WWW 2019, link
  • Depeng Xu, Shuhan Yuan and Xintao Wu, Achieving Differential Privacy and Fairness in Logistic Regression, in WWW 2019 workshop on FATES, link
  • Depeng Xu, Shuhan Yuan, Lu Zhang, and Xintao Wu, FairGAN+: Achieving Fair Data Generation and Fair Classification through Generative Adversarial Networks, in KDD 2019 workshop on XAI
  • Depeng Xu, Yongkai Wu, Shuhan Yuan, Lu Zhang, and Xintao Wu, Achieving Causal Fairness through Generative Adversarial Networks, in IJCAI 2019, link
  • Yongkai Wu, Lu Zhang, and Xintao Wu, Counterfactual Fairness: Unidentification, Bound and Algorithm, in IJCAI 2019, link

Tutorials by SAIL