{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:RBFSSCQZZMZINSX5J6V723ZADN","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":"71a7bbf701e2d56bade1f744d03e9e1c28c5901bc6d1c765a5e2583a44eb9b26","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-07-31T17:55:16Z","title_canon_sha256":"5c13fc3e5422c07c305fbc7df36d4f86ad606925ede11d43b18b09e91a263938"},"schema_version":"1.0","source":{"id":"2507.23768","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.23768","created_at":"2026-07-05T11:46:30Z"},{"alias_kind":"arxiv_version","alias_value":"2507.23768v1","created_at":"2026-07-05T11:46:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.23768","created_at":"2026-07-05T11:46:30Z"},{"alias_kind":"pith_short_12","alias_value":"RBFSSCQZZMZI","created_at":"2026-07-05T11:46:30Z"},{"alias_kind":"pith_short_16","alias_value":"RBFSSCQZZMZINSX5","created_at":"2026-07-05T11:46:30Z"},{"alias_kind":"pith_short_8","alias_value":"RBFSSCQZ","created_at":"2026-07-05T11:46:30Z"}],"graph_snapshots":[{"event_id":"sha256:449887b193e72a190639178854a5f8bd33099882eb0fcc0d5645ac90d89a995b","target":"graph","created_at":"2026-07-05T11:46:30Z","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/2507.23768/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In analyses with severe data-limitations, augmenting the target dataset with information from ancillary datasets in the application domain, called source datasets, can lead to significantly improved statistical procedures. However, existing methods for this transfer learning struggle to deal with situations where the source datasets are also limited and not guaranteed to be well-aligned with the target dataset. A typical strategy is to use the empirical loss minimizer on the source data as a prior mean for the target parameters, which places the estimation of source parameters outside of the B","authors_text":"Ali Arab, Lisa O. Singh, Nathan Wycoff","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-07-31T17:55:16Z","title":"Formal Bayesian Transfer Learning via the Total Risk Prior"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.23768","kind":"arxiv","version":1},"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:aee3c5ea05c811a2a5dfc51bd510d32991fe6ed85f6acb5f4f1d04c428b04b59","target":"record","created_at":"2026-07-05T11:46:30Z","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":"71a7bbf701e2d56bade1f744d03e9e1c28c5901bc6d1c765a5e2583a44eb9b26","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-07-31T17:55:16Z","title_canon_sha256":"5c13fc3e5422c07c305fbc7df36d4f86ad606925ede11d43b18b09e91a263938"},"schema_version":"1.0","source":{"id":"2507.23768","kind":"arxiv","version":1}},"canonical_sha256":"884b290a19cb3286cafd4fabfd6f201b50bf0d5725731c3d1c13b3791103348d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"884b290a19cb3286cafd4fabfd6f201b50bf0d5725731c3d1c13b3791103348d","first_computed_at":"2026-07-05T11:46:30.644426Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:46:30.644426Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WPG7Iq/nksNSrJGQ6MyPFIQCeguId/3mzLTLYqQtzJn13X6P55Y/E/X08g9Fb7bR7x7BVWhKmbLK51JiF7qpCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:46:30.644934Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.23768","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:aee3c5ea05c811a2a5dfc51bd510d32991fe6ed85f6acb5f4f1d04c428b04b59","sha256:449887b193e72a190639178854a5f8bd33099882eb0fcc0d5645ac90d89a995b"],"state_sha256":"da501de9d8404ba1739e781d81282f6092223f43f60def653416a923dcca8cd9"}