5 papers
Fairness May Backfire: When Leveling-Down Occurs in Fair Machine Learning
Yi Yang, Xiangyu Chang, Pei-yu Chen
As machine learning (ML) systems increasingly shape access to credit, jobs, and other opportunities, the fairness of algorithmic decisions has become a central concern. Yet it rema…
Beyond Cross-Validation: Adaptive Parameter Selection for Kernel-Based Gradient Descents
Xiaotong Liu, Yunwen Lei, Xiangyu Chang +1
This paper proposes a novel parameter selection strategy for kernel-based gradient descent (KGD) algorithms, integrating bias-variance analysis with the splitting method. We introd…
A New Causal Rule Learning Approach to Interpretable Estimation of Heterogeneous Treatment Effect
Ying Wu, Hanzhong Liu, Kai Ren +2
Interpretability plays a crucial role in the application of statistical learning to estimate heterogeneous treatment effects (HTE) in complex diseases. In this study, we leverage a…
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…
Beyond Uniform Deletion: A Data Value-Weighted Framework for Certified Machine Unlearning
Lisong He, Yi Yang, Xiangyu Chang
As the right to be forgotten becomes legislated worldwide, machine unlearning mechanisms have emerged to efficiently update models for data deletion and enhance user privacy protec…