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 kim and schrab

A Unified View of Optimal Kernel Hypothesis Testing

Schrab, Antonin

arXiv.org Machine Learning

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 separation rates in the kernel and $L^2$ metrics are presented, with two adaptive kernel selection methods (kernel pooling and aggregation), and under various testing constraints: computational efficiency, differential privacy, and robustness to data corruption. Intuition behind the derivation of the power results is provided in a unified way accross the three frameworks, and open problems are highlighted.

  kim and schrab, log null 1, schrab, (12 more...)
2503.07084
  Country:
  Genre: Research Report (1.00)
  Industry: Information Technology > Security & Privacy (0.45)
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