1 citations · 2 across the 5 of their papers we have counts for
5 papers
Incentivized Exploration with Stochastic Covariates: A Two-Stage Mechanism Design for Recommender System
Yuantong Li, Guang Cheng, Xiaowu Dai
Recommender systems play a crucial role in internet economies by connecting users with relevant products. However, designing effective recommender systems faces the key challenges:…
Tree-based Ensemble Learning for Out-of-distribution Detection
Zhaiming Shen, Menglun Wang, Guang Cheng +5
Being able to successfully determine whether the testing samples has similar distribution as the training samples is a fundamental question to address before we can safely deploy m…
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 \…
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…
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…