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cs.LG2025
DUAL: Learning Diverse Kernels for Aggregated Two-sample and Independence Testing
Zhijian Zhou, Xunye Tian, Liuhua Peng +4
To adapt kernel two-sample and independence testing to complex structured data, aggregation of multiple kernels is frequently employed to boost testing power compared to single-ker…
stat.ML2025
A Unified View of Optimal Kernel Hypothesis Testing
Antonin Schrab
This paper provides a unifying view of optimal kernel hypothesis testing across the MMD two-sample, HSIC independence, and KSD goodness-of-fit frameworks. Minimax optimal separatio…
stat.ML2025
A Practical Introduction to Kernel Discrepancies: MMD, HSIC & KSD
Antonin Schrab
This article provides a practical introduction to kernel discrepancies, focusing on the Maximum Mean Discrepancy (MMD), the Hilbert-Schmidt Independence Criterion (HSIC), and the K…