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stat.ML2025
Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing
Xianli Zeng, Kevin Jiang, Guang Cheng +1
Machine learning algorithms may have disparate impacts on protected groups. To address this, we develop methods for Bayes-optimal fair classification, aiming to minimize classifica…
stat.ML2024
Minimax Optimal Fair Classification with Bounded Demographic Disparity
Xianli Zeng, Guang Cheng, Edgar Dobriban
Mitigating the disparate impact of statistical machine learning methods is crucial for ensuring fairness. While extensive research aims to reduce disparity, the effect of using a \…
stat.ML2024
FairRR: Pre-Processing for Group Fairness through Randomized Response
Xianli Zeng, Joshua Ward, Guang Cheng
The increasing usage of machine learning models in consequential decision-making processes has spurred research into the fairness of these systems. While significant work has been…