1 citations · 1 across the 4 of their papers we have counts for
4 papers
Rethinking Data Value: Asymmetric Data Shapley for Structure-Aware Valuation in Data Markets and Machine Learning Pipelines
Xi Zheng, Yinghui Huang, Xiangyu Chang +2
Rigorous valuation of individual data sources is critical for fair compensation in data markets, informed data acquisition, and transparent development of ML/AI models. Classical D…
Bayes-Optimal Fair Classification with Multiple Sensitive Features
Yi Yang, Yinghui Huang, Xiangyu Chang
Existing theoretical work on Bayes-optimal fair classifiers usually considers a single (binary) sensitive feature. In practice, individuals are often defined by multiple sensitive…
An Enhanced Zeroth-Order Stochastic Frank-Wolfe Framework for Constrained Finite-Sum Optimization
Haishan Ye, Yinghui Huang, Hao Di +1
We propose an enhanced zeroth-order stochastic Frank-Wolfe framework to address constrained finite-sum optimization problems, a structure prevalent in large-scale machine-learning…
AdapFair: Ensuring Adaptive Fairness for Machine Learning Operations
Yinghui Huang, Zihao Tang, Xiangyu Chang
The biases and discrimination of machine learning algorithms have attracted significant attention, leading to the development of various algorithms tailored to specific contexts. H…