Mixture Proportion Estimation and Weakly-supervised Kernel Test for Conditional Independence

• URL: http://arxiv.org/abs/2604.07191v1
• Date: Wed, 08 Apr 2026 15:17:19 GMT
• Title: Mixture Proportion Estimation and Weakly-supervised Kernel Test for Conditional Independence
• Abstract summary: Mixture proportion estimation (MPE) aims to estimate class priors from unlabeled data.<n>Existing MPE methods rely on the textitirreducibility assumption or its variant for identifiability.<n>We propose novel assumptions based on conditional independence (CI) given the class label, which ensure identifiability even when irreducibility does not hold.
• Score: 1.0227479910430863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mixture proportion estimation (MPE) aims to estimate class priors from unlabeled data. This task is a critical component in weakly supervised learning, such as PU learning, learning with label noise, and domain adaptation. Existing MPE methods rely on the \textit{irreducibility} assumption or its variant for identifiability. In this paper, we propose novel assumptions based on conditional independence (CI) given the class label, which ensure identifiability even when irreducibility does not hold. We develop method of moments estimators under these assumptions and analyze their asymptotic properties. Furthermore, we present weakly-supervised kernel tests to validate the CI assumptions, which are of independent interest in applications such as causal discovery and fairness evaluation. Empirically, we demonstrate the improved performance of our estimators compared with existing methods and that our tests successfully control both type I and type II errors.\label{key}

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