{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WKKAA2VFJ6Y2YXR6CRE44KFHQ2","short_pith_number":"pith:WKKAA2VF","schema_version":"1.0","canonical_sha256":"b294006aa54fb1ac5e3e1449ce28a7869f37d65c2dd465b74e5baca8102ce63a","source":{"kind":"arxiv","id":"2406.00701","version":2},"attestation_state":"computed","paper":{"title":"Profiled Transfer Learning for High Dimensional Linear Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Fang Wang, Hansheng Wang, Junlong Zhao, Ziqian Lin","submitted_at":"2024-06-02T10:36:28Z","abstract_excerpt":"We develop here a novel transfer learning methodology called Profiled Transfer Learning (PTL). The method is based on the \\textit{approximate-linear} assumption between the source and target parameters. Compared with the commonly assumed \\textit{vanishing-difference} assumption and \\textit{low-rank} assumption in the literature, the \\textit{approximate-linear} assumption is more flexible and less stringent. Specifically, the PTL estimator is constructed by two major steps. Firstly, we regress the response on the transferred feature, leading to the profiled responses. Subsequently, we learn the"},"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":"2406.00701","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-06-02T10:36:28Z","cross_cats_sorted":["stat.ME","stat.TH"],"title_canon_sha256":"acb78609467f01a972136f24bed02d9731d934028347cb1cf1afeddd86739139","abstract_canon_sha256":"4161935c7cd2283d7bd6512db7a69cc71576fcc0ef9f98de26cb367609d4a514"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:27:49.507898Z","signature_b64":"WuzwGGk5ufF7soPlHt/C5Eqp7l5/EmTLMUqrQN8EHcnuEGxNeUIZ1LWrR144flisPRWXXO7cTvOsDkKqlmq6AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b294006aa54fb1ac5e3e1449ce28a7869f37d65c2dd465b74e5baca8102ce63a","last_reissued_at":"2026-07-05T08:27:49.507395Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:27:49.507395Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Profiled Transfer Learning for High Dimensional Linear Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Fang Wang, Hansheng Wang, Junlong Zhao, Ziqian Lin","submitted_at":"2024-06-02T10:36:28Z","abstract_excerpt":"We develop here a novel transfer learning methodology called Profiled Transfer Learning (PTL). The method is based on the \\textit{approximate-linear} assumption between the source and target parameters. Compared with the commonly assumed \\textit{vanishing-difference} assumption and \\textit{low-rank} assumption in the literature, the \\textit{approximate-linear} assumption is more flexible and less stringent. Specifically, the PTL estimator is constructed by two major steps. Firstly, we regress the response on the transferred feature, leading to the profiled responses. Subsequently, we learn the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.00701","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/2406.00701/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":"2406.00701","created_at":"2026-07-05T08:27:49.507460+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.00701v2","created_at":"2026-07-05T08:27:49.507460+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.00701","created_at":"2026-07-05T08:27:49.507460+00:00"},{"alias_kind":"pith_short_12","alias_value":"WKKAA2VFJ6Y2","created_at":"2026-07-05T08:27:49.507460+00:00"},{"alias_kind":"pith_short_16","alias_value":"WKKAA2VFJ6Y2YXR6","created_at":"2026-07-05T08:27:49.507460+00:00"},{"alias_kind":"pith_short_8","alias_value":"WKKAA2VF","created_at":"2026-07-05T08:27:49.507460+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.11622","citing_title":"Deconfounding via Profiled Transfer Learning","ref_index":1971,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WKKAA2VFJ6Y2YXR6CRE44KFHQ2","json":"https://pith.science/pith/WKKAA2VFJ6Y2YXR6CRE44KFHQ2.json","graph_json":"https://pith.science/api/pith-number/WKKAA2VFJ6Y2YXR6CRE44KFHQ2/graph.json","events_json":"https://pith.science/api/pith-number/WKKAA2VFJ6Y2YXR6CRE44KFHQ2/events.json","paper":"https://pith.science/paper/WKKAA2VF"},"agent_actions":{"view_html":"https://pith.science/pith/WKKAA2VFJ6Y2YXR6CRE44KFHQ2","download_json":"https://pith.science/pith/WKKAA2VFJ6Y2YXR6CRE44KFHQ2.json","view_paper":"https://pith.science/paper/WKKAA2VF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.00701&json=true","fetch_graph":"https://pith.science/api/pith-number/WKKAA2VFJ6Y2YXR6CRE44KFHQ2/graph.json","fetch_events":"https://pith.science/api/pith-number/WKKAA2VFJ6Y2YXR6CRE44KFHQ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WKKAA2VFJ6Y2YXR6CRE44KFHQ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WKKAA2VFJ6Y2YXR6CRE44KFHQ2/action/storage_attestation","attest_author":"https://pith.science/pith/WKKAA2VFJ6Y2YXR6CRE44KFHQ2/action/author_attestation","sign_citation":"https://pith.science/pith/WKKAA2VFJ6Y2YXR6CRE44KFHQ2/action/citation_signature","submit_replication":"https://pith.science/pith/WKKAA2VFJ6Y2YXR6CRE44KFHQ2/action/replication_record"}},"created_at":"2026-07-05T08:27:49.507460+00:00","updated_at":"2026-07-05T08:27:49.507460+00:00"}