{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:5SVU23BCB4UKTYDC2GQX26LLKJ","short_pith_number":"pith:5SVU23BC","schema_version":"1.0","canonical_sha256":"ecab4d6c220f28a9e062d1a17d796b527fdc2970e5b7337090e53f5efb371dd7","source":{"kind":"arxiv","id":"2006.13228","version":2},"attestation_state":"computed","paper":{"title":"A General Class of Transfer Learning Regression without Implementation Cost","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Kenji Fukumizu, Ryo Yoshida, Shunya Minami, Song Liu, Stephen Wu","submitted_at":"2020-06-23T18:00:02Z","abstract_excerpt":"We propose a novel framework that unifies and extends existing methods of transfer learning (TL) for regression. To bridge a pretrained source model to the model on a target task, we introduce a density-ratio reweighting function, which is estimated through the Bayesian framework with a specific prior distribution. By changing two intrinsic hyperparameters and the choice of the density-ratio model, the proposed method can integrate three popular methods of TL: TL based on cross-domain similarity regularization, a probabilistic TL using the density-ratio estimation, and fine-tuning of pretraine"},"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":"2006.13228","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-06-23T18:00:02Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b9126a9d7c603a83b458e0068556523d7662a488128e266e4b54237df25d2bc0","abstract_canon_sha256":"df156045fc394415256e89595833d71f8123d5369509cc3b5a24f4d6d95be2fc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:00:11.584755Z","signature_b64":"FhoB9geq9kdE/lWMcJIedX95jFQnaWE6PdjbQzzrMS6bYKsiwzGHyI59ZpF4QhU6bONywU+IZdlK3mer/UcTBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ecab4d6c220f28a9e062d1a17d796b527fdc2970e5b7337090e53f5efb371dd7","last_reissued_at":"2026-07-05T02:00:11.584270Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:00:11.584270Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A General Class of Transfer Learning Regression without Implementation Cost","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Kenji Fukumizu, Ryo Yoshida, Shunya Minami, Song Liu, Stephen Wu","submitted_at":"2020-06-23T18:00:02Z","abstract_excerpt":"We propose a novel framework that unifies and extends existing methods of transfer learning (TL) for regression. To bridge a pretrained source model to the model on a target task, we introduce a density-ratio reweighting function, which is estimated through the Bayesian framework with a specific prior distribution. By changing two intrinsic hyperparameters and the choice of the density-ratio model, the proposed method can integrate three popular methods of TL: TL based on cross-domain similarity regularization, a probabilistic TL using the density-ratio estimation, and fine-tuning of pretraine"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.13228","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/2006.13228/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":"2006.13228","created_at":"2026-07-05T02:00:11.584341+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.13228v2","created_at":"2026-07-05T02:00:11.584341+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.13228","created_at":"2026-07-05T02:00:11.584341+00:00"},{"alias_kind":"pith_short_12","alias_value":"5SVU23BCB4UK","created_at":"2026-07-05T02:00:11.584341+00:00"},{"alias_kind":"pith_short_16","alias_value":"5SVU23BCB4UKTYDC","created_at":"2026-07-05T02:00:11.584341+00:00"},{"alias_kind":"pith_short_8","alias_value":"5SVU23BC","created_at":"2026-07-05T02:00:11.584341+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5SVU23BCB4UKTYDC2GQX26LLKJ","json":"https://pith.science/pith/5SVU23BCB4UKTYDC2GQX26LLKJ.json","graph_json":"https://pith.science/api/pith-number/5SVU23BCB4UKTYDC2GQX26LLKJ/graph.json","events_json":"https://pith.science/api/pith-number/5SVU23BCB4UKTYDC2GQX26LLKJ/events.json","paper":"https://pith.science/paper/5SVU23BC"},"agent_actions":{"view_html":"https://pith.science/pith/5SVU23BCB4UKTYDC2GQX26LLKJ","download_json":"https://pith.science/pith/5SVU23BCB4UKTYDC2GQX26LLKJ.json","view_paper":"https://pith.science/paper/5SVU23BC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.13228&json=true","fetch_graph":"https://pith.science/api/pith-number/5SVU23BCB4UKTYDC2GQX26LLKJ/graph.json","fetch_events":"https://pith.science/api/pith-number/5SVU23BCB4UKTYDC2GQX26LLKJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5SVU23BCB4UKTYDC2GQX26LLKJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5SVU23BCB4UKTYDC2GQX26LLKJ/action/storage_attestation","attest_author":"https://pith.science/pith/5SVU23BCB4UKTYDC2GQX26LLKJ/action/author_attestation","sign_citation":"https://pith.science/pith/5SVU23BCB4UKTYDC2GQX26LLKJ/action/citation_signature","submit_replication":"https://pith.science/pith/5SVU23BCB4UKTYDC2GQX26LLKJ/action/replication_record"}},"created_at":"2026-07-05T02:00:11.584341+00:00","updated_at":"2026-07-05T02:00:11.584341+00:00"}