{"paper":{"title":"Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Alexander Korotin, Anastasis Kratsios, Arip Asadulaev, Dmitry Baranchuk, Evgeny Burnaev, Mikhail Persiianov, Nikita Andreev, Nikita Starodubcev","submitted_at":"2024-10-03T16:12:59Z","abstract_excerpt":"Learning conditional distributions $\\pi^*(\\cdot|x)$ is a central problem in machine learning, which is typically approached via supervised methods with paired data $(x,y) \\sim \\pi^*$. However, acquiring paired data samples is often challenging, especially in problems such as domain translation. This necessitates the development of $\\textit{semi-supervised}$ models that utilize both limited paired data and additional unpaired i.i.d. samples $x \\sim \\pi^*_x$ and $y \\sim \\pi^*_y$ from the marginal distributions. The usage of such combined data is complex and often relies on heuristic approaches. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.02628","kind":"arxiv","version":5},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2410.02628/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}