#conditional-distributions News & Analysis

Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization

Researchers propose EBiEOT, a novel semi-supervised learning framework that leverages both paired and unpaired data through likelihood maximization and inverse entropic optimal transport. The method demonstrates universal approximation properties and provides an end-to-end algorithm for learning conditional distributions, with potential applications in domain translation and other data-scarce scenarios.