{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/inverse-entropic-optimal-transport-solves","title":"Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization","arxiv_id":"2410.02628","date":"2024-10-03","proceeding":null,"authors":["Mikhail Persiianov","Arip Asadulaev","Nikita Andreev","Nikita Starodubcev","Dmitry Baranchuk","Anastasis Kratsios","Evgeny Burnaev","Alexander Korotin"],"abstract":"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. To tackle this issue, we propose a new learning paradigm that integrates both paired and unpaired data $\\textbf{seamlessly}$ through the data likelihood maximization techniques. We demonstrate that our approach also connects intriguingly with inverse entropic optimal transport (OT). This finding allows us to apply recent advances in computational OT to establish a $\\textbf{light}$ learning algorithm to get $\\pi^*(\\cdot|x)$. Furthermore, we demonstrate through empirical tests that our method effectively learns conditional distributions using paired and unpaired data simultaneously.","url_abs":"https://arxiv.org/abs/2410.02628v2","url_pdf":"https://arxiv.org/pdf/2410.02628v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2410.02628","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.02628"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/MuXauJl11110/EBiEOT","reach":null}],"summary":{"unverified":1},"by_repo_kind":{"found_in_text":{"samples":1,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"49eaa92ebfb4b744","entry":"BaseEBiEOT","repo":"MuXauJl11110/EBiEOT","repo_kind":"found_in_text","path":"src/ebieot/base.py","file_url":"https://github.com/MuXauJl11110/EBiEOT/blob/HEAD/src/ebieot/base.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"49eaa92ebfb4b744"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}