Invariant Feature Extraction Through Conditional Independence and the Optimal Transport Barycenter Problem: the Gaussian case

• URL: http://arxiv.org/abs/2512.20914v1
• Date: Wed, 24 Dec 2025 03:39:18 GMT
• Title: Invariant Feature Extraction Through Conditional Independence and the Optimal Transport Barycenter Problem: the Gaussian case
• Authors: Ian Bounos, Pablo Groisman, Mariela Sued, Esteban Tabak,
• Abstract summary: A methodology is developed to extract $d$ invariant features $W=f(X)$ that predict a response variable $Y$ without being confounded by variables $Z$.<n>The main ingredient is the penalization of any statistical dependence between $W$ and $Z$ conditioned on $Y$.<n>The procedure extends with little change to more general, non-Gaussian / non-linear cases.
• Score: 0.4899818550820576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A methodology is developed to extract $d$ invariant features $W=f(X)$ that predict a response variable $Y$ without being confounded by variables $Z$ that may influence both $X$ and $Y$. The methodology's main ingredient is the penalization of any statistical dependence between $W$ and $Z$ conditioned on $Y$, replaced by the more readily implementable plain independence between $W$ and the random variable $Z_Y = T(Z,Y)$ that solves the [Monge] Optimal Transport Barycenter Problem for $Z\mid Y$. In the Gaussian case considered in this article, the two statements are equivalent. When the true confounders $Z$ are unknown, other measurable contextual variables $S$ can be used as surrogates, a replacement that involves no relaxation in the Gaussian case if the covariance matrix $Σ_{ZS}$ has full range. The resulting linear feature extractor adopts a closed form in terms of the first $d$ eigenvectors of a known matrix. The procedure extends with little change to more general, non-Gaussian / non-linear cases.

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