Measuring Differences between Conditional Distributions using Kernel Embeddings
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. This paper addresses this gap by establishing a coherent framework for studying kernel-based methods to measure divergence between conditional distributions through what we refer to as conditional maximum mean discrepancy (CMMD). The CMMD consists of a family of metrics which we c...
| Authors | Peter Moskvichev, Siu Lun Chau, Dino Sejdinovic |
|---|---|
| Primary category | stat.ML |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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