{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:CFPHZXO6IWCU2AKQEPPDOYAHLJ","short_pith_number":"pith:CFPHZXO6","schema_version":"1.0","canonical_sha256":"115e7cddde45854d015023de3760075a79d17e2ca98dbecbf65aea0d530c6a46","source":{"kind":"arxiv","id":"2103.11937","version":2},"attestation_state":"computed","paper":{"title":"Regularized Optimal Transport for Dynamic Semi-supervised Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Mourad El Hamri, Youn\\`es Bennani","submitted_at":"2021-03-22T15:31:53Z","abstract_excerpt":"Semi-supervised learning provides an effective paradigm for leveraging unlabeled data to improve a model's performance. Among the many strategies proposed, graph-based methods have shown excellent properties, in particular since they allow to solve directly the transductive tasks according to Vapnik's principle and they can be extended efficiently for inductive tasks. In this paper, we propose a novel approach for the transductive semi-supervised learning, using a complete bipartite edge-weighted graph. The proposed approach uses the regularized optimal transport between empirical measures def"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2103.11937","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2021-03-22T15:31:53Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"31071493eb4cef106c0d32f0a70d8361753d897525927f5ec905222970700376","abstract_canon_sha256":"86e641672b58fdb56533e6d193360c5ba8c5614aa34fc87d7dc4f848fe5cbe74"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:26:29.537041Z","signature_b64":"mNhekmqqmRIhmzRr16rJOAy//34rUSjlglc1Pt9vzzgbNZsDg7Ld68npdzCFgcep34YeiXC5jXdiCneFMysBCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"115e7cddde45854d015023de3760075a79d17e2ca98dbecbf65aea0d530c6a46","last_reissued_at":"2026-07-05T02:26:29.536577Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:26:29.536577Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Regularized Optimal Transport for Dynamic Semi-supervised Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Mourad El Hamri, Youn\\`es Bennani","submitted_at":"2021-03-22T15:31:53Z","abstract_excerpt":"Semi-supervised learning provides an effective paradigm for leveraging unlabeled data to improve a model's performance. Among the many strategies proposed, graph-based methods have shown excellent properties, in particular since they allow to solve directly the transductive tasks according to Vapnik's principle and they can be extended efficiently for inductive tasks. In this paper, we propose a novel approach for the transductive semi-supervised learning, using a complete bipartite edge-weighted graph. The proposed approach uses the regularized optimal transport between empirical measures def"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.11937","kind":"arxiv","version":2},"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/2103.11937/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2103.11937","created_at":"2026-07-05T02:26:29.536634+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.11937v2","created_at":"2026-07-05T02:26:29.536634+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.11937","created_at":"2026-07-05T02:26:29.536634+00:00"},{"alias_kind":"pith_short_12","alias_value":"CFPHZXO6IWCU","created_at":"2026-07-05T02:26:29.536634+00:00"},{"alias_kind":"pith_short_16","alias_value":"CFPHZXO6IWCU2AKQ","created_at":"2026-07-05T02:26:29.536634+00:00"},{"alias_kind":"pith_short_8","alias_value":"CFPHZXO6","created_at":"2026-07-05T02:26:29.536634+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.09362","citing_title":"A Revisit to Rate-distortion Theory via Optimal Weak Transport","ref_index":24,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CFPHZXO6IWCU2AKQEPPDOYAHLJ","json":"https://pith.science/pith/CFPHZXO6IWCU2AKQEPPDOYAHLJ.json","graph_json":"https://pith.science/api/pith-number/CFPHZXO6IWCU2AKQEPPDOYAHLJ/graph.json","events_json":"https://pith.science/api/pith-number/CFPHZXO6IWCU2AKQEPPDOYAHLJ/events.json","paper":"https://pith.science/paper/CFPHZXO6"},"agent_actions":{"view_html":"https://pith.science/pith/CFPHZXO6IWCU2AKQEPPDOYAHLJ","download_json":"https://pith.science/pith/CFPHZXO6IWCU2AKQEPPDOYAHLJ.json","view_paper":"https://pith.science/paper/CFPHZXO6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.11937&json=true","fetch_graph":"https://pith.science/api/pith-number/CFPHZXO6IWCU2AKQEPPDOYAHLJ/graph.json","fetch_events":"https://pith.science/api/pith-number/CFPHZXO6IWCU2AKQEPPDOYAHLJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CFPHZXO6IWCU2AKQEPPDOYAHLJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CFPHZXO6IWCU2AKQEPPDOYAHLJ/action/storage_attestation","attest_author":"https://pith.science/pith/CFPHZXO6IWCU2AKQEPPDOYAHLJ/action/author_attestation","sign_citation":"https://pith.science/pith/CFPHZXO6IWCU2AKQEPPDOYAHLJ/action/citation_signature","submit_replication":"https://pith.science/pith/CFPHZXO6IWCU2AKQEPPDOYAHLJ/action/replication_record"}},"created_at":"2026-07-05T02:26:29.536634+00:00","updated_at":"2026-07-05T02:26:29.536634+00:00"}