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stat.ML2026
Kernel Selection is Model Selection: A Unified Complexity-Penalized Approach for MMD Two-Sample Tests
Yijin Ni, Xiaoming Huo
The Maximum Mean Discrepancy (MMD) is a cornerstone statistic for nonparametric two-sample testing, but its test power is dictated entirely by the chosen kernel. Because any fixed…
stat.ML2025
Kernel-based Equalized Odds: A Quantification of Accuracy-Fairness Trade-off in Fair Representation Learning
Yijin Ni, Xiaoming Huo
This paper introduces a novel kernel-based formulation of the Equalized Odds (EO) criterion, denoted as , for fair representation learning (FRL) in supervised settings. The c…