AI RESEARCH
Measuring Differences between Conditional Distributions using Kernel Embeddings
arXiv CS.LG
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ArXi:2605.02260v1 Announce Type: cross Comparing conditional distributions is a fundamental challenge in statistics and machine learning, with applications across a wide range of domains. While proposed methods for measuring discrepancies using kernel embeddings of distributions in a reproducing kernel Hilbert space (RKHS) provide powerful non-parametric techniques, the existing literature remains fragmented and lacks a unified theoretical treatment.