{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:WKKAA2VFJ6Y2YXR6CRE44KFHQ2","short_pith_number":"pith:WKKAA2VF","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"},"canonical_sha256":"b294006aa54fb1ac5e3e1449ce28a7869f37d65c2dd465b74e5baca8102ce63a","source":{"kind":"arxiv","id":"2406.00701","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.00701","created_at":"2026-07-05T08:27:49Z"},{"alias_kind":"arxiv_version","alias_value":"2406.00701v2","created_at":"2026-07-05T08:27:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.00701","created_at":"2026-07-05T08:27:49Z"},{"alias_kind":"pith_short_12","alias_value":"WKKAA2VFJ6Y2","created_at":"2026-07-05T08:27:49Z"},{"alias_kind":"pith_short_16","alias_value":"WKKAA2VFJ6Y2YXR6","created_at":"2026-07-05T08:27:49Z"},{"alias_kind":"pith_short_8","alias_value":"WKKAA2VF","created_at":"2026-07-05T08:27:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:WKKAA2VFJ6Y2YXR6CRE44KFHQ2","target":"record","payload":{"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"},"canonical_sha256":"b294006aa54fb1ac5e3e1449ce28a7869f37d65c2dd465b74e5baca8102ce63a","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"},"source_kind":"arxiv","source_id":"2406.00701","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:27:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g9KHe7aCjuL3wTrBxtiTxi/lYm4Yf4KsTCNdC7nfxjiknsFqkFCX/+/3KVKs/x335GypT0OUFsNQNH02sQK0Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T12:31:21.945092Z"},"content_sha256":"853f477b4759c99d6828f8331edb5ccb49d39cf686acb3abbc38e6f2fac529f0","schema_version":"1.0","event_id":"sha256:853f477b4759c99d6828f8331edb5ccb49d39cf686acb3abbc38e6f2fac529f0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:WKKAA2VFJ6Y2YXR6CRE44KFHQ2","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:27:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aoLKs2N+WR0W54JAaLo9iHJGJlP6Oj2QU2A3TDI5/PCsgQgIEianJJ3lqJdzsJe39ofpzdYYgjimd8IfZTtDAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T12:31:21.945609Z"},"content_sha256":"b83690a64f2d34886e190cd80c434ea85ff921a8db1cca8481438dfbe873406c","schema_version":"1.0","event_id":"sha256:b83690a64f2d34886e190cd80c434ea85ff921a8db1cca8481438dfbe873406c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WKKAA2VFJ6Y2YXR6CRE44KFHQ2/bundle.json","state_url":"https://pith.science/pith/WKKAA2VFJ6Y2YXR6CRE44KFHQ2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WKKAA2VFJ6Y2YXR6CRE44KFHQ2/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T12:31:21Z","links":{"resolver":"https://pith.science/pith/WKKAA2VFJ6Y2YXR6CRE44KFHQ2","bundle":"https://pith.science/pith/WKKAA2VFJ6Y2YXR6CRE44KFHQ2/bundle.json","state":"https://pith.science/pith/WKKAA2VFJ6Y2YXR6CRE44KFHQ2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WKKAA2VFJ6Y2YXR6CRE44KFHQ2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:WKKAA2VFJ6Y2YXR6CRE44KFHQ2","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"4161935c7cd2283d7bd6512db7a69cc71576fcc0ef9f98de26cb367609d4a514","cross_cats_sorted":["stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-06-02T10:36:28Z","title_canon_sha256":"acb78609467f01a972136f24bed02d9731d934028347cb1cf1afeddd86739139"},"schema_version":"1.0","source":{"id":"2406.00701","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.00701","created_at":"2026-07-05T08:27:49Z"},{"alias_kind":"arxiv_version","alias_value":"2406.00701v2","created_at":"2026-07-05T08:27:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.00701","created_at":"2026-07-05T08:27:49Z"},{"alias_kind":"pith_short_12","alias_value":"WKKAA2VFJ6Y2","created_at":"2026-07-05T08:27:49Z"},{"alias_kind":"pith_short_16","alias_value":"WKKAA2VFJ6Y2YXR6","created_at":"2026-07-05T08:27:49Z"},{"alias_kind":"pith_short_8","alias_value":"WKKAA2VF","created_at":"2026-07-05T08:27:49Z"}],"graph_snapshots":[{"event_id":"sha256:b83690a64f2d34886e190cd80c434ea85ff921a8db1cca8481438dfbe873406c","target":"graph","created_at":"2026-07-05T08:27:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2406.00701/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Fang Wang, Hansheng Wang, Junlong Zhao, Ziqian Lin","cross_cats":["stat.ME","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-06-02T10:36:28Z","title":"Profiled Transfer Learning for High Dimensional Linear Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.00701","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:853f477b4759c99d6828f8331edb5ccb49d39cf686acb3abbc38e6f2fac529f0","target":"record","created_at":"2026-07-05T08:27:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"4161935c7cd2283d7bd6512db7a69cc71576fcc0ef9f98de26cb367609d4a514","cross_cats_sorted":["stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-06-02T10:36:28Z","title_canon_sha256":"acb78609467f01a972136f24bed02d9731d934028347cb1cf1afeddd86739139"},"schema_version":"1.0","source":{"id":"2406.00701","kind":"arxiv","version":2}},"canonical_sha256":"b294006aa54fb1ac5e3e1449ce28a7869f37d65c2dd465b74e5baca8102ce63a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b294006aa54fb1ac5e3e1449ce28a7869f37d65c2dd465b74e5baca8102ce63a","first_computed_at":"2026-07-05T08:27:49.507395Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:27:49.507395Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WuzwGGk5ufF7soPlHt/C5Eqp7l5/EmTLMUqrQN8EHcnuEGxNeUIZ1LWrR144flisPRWXXO7cTvOsDkKqlmq6AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:27:49.507898Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.00701","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:853f477b4759c99d6828f8331edb5ccb49d39cf686acb3abbc38e6f2fac529f0","sha256:b83690a64f2d34886e190cd80c434ea85ff921a8db1cca8481438dfbe873406c"],"state_sha256":"2d899368ef47fc823153a7ed996e2dc1e1b2c20b3350f7471b26aaf282ebef95"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ui+B8cQ78PZcMoIePH2tRAquiXSNOuXREiPzSy3UcUWNWPIEg6X5FlpQvQ8ZuGSRaGfrYJ6nABuZDvPUeTO1DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T12:31:21.949796Z","bundle_sha256":"920a8d13fe908d1fe5a3254a256f7a65fc91e3f6951dd586689a968d780baa53"}}