Kernel conditional tests from learning-theoretic bounds

We propose a framework for hypothesis testing on conditional probability distributions, which we then use to construct . These tests identify the inputs where the functionals differ with high probability, and include tests of conditional moments or two-sample tests. Our key idea is to transform confidence bounds of a learning method into a test of conditional expectations.