{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HVNBZQW55WZTM3ZSXOOPMZBUWS","short_pith_number":"pith:HVNBZQW5","schema_version":"1.0","canonical_sha256":"3d5a1cc2ddedb3366f32bb9cf66434b49a1a5de72a20d91d3278ef3b54046425","source":{"kind":"arxiv","id":"2403.07797","version":1},"attestation_state":"computed","paper":{"title":"Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Brett Mullins, Daniel Sheldon, Gerome Miklau, Miguel Fuentes, Ryan McKenna","submitted_at":"2024-03-12T16:34:07Z","abstract_excerpt":"Mechanisms for generating differentially private synthetic data based on marginals and graphical models have been successful in a wide range of settings. However, one limitation of these methods is their inability to incorporate public data. Initializing a data generating model by pre-training on public data has shown to improve the quality of synthetic data, but this technique is not applicable when model structure is not determined a priori. We develop the mechanism jam-pgm, which expands the adaptive measurements framework to jointly select between measuring public data and private data. Th"},"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":"2403.07797","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-12T16:34:07Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b093d19837103290459a1ab900ad47f20f876f5934a077204db468774bf8f590","abstract_canon_sha256":"2b51e1c8009d6d763870277ebc185a92fc563aa97993514a97ada7deb58fdc30"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:55:15.563290Z","signature_b64":"uLoH6572DGyOIVHLlj3GQq2SUfts9mysENh/EbMO7irBLAfCCzRk3LxCvYaiYFkj3oeco8+Aj7noxime2x0vBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d5a1cc2ddedb3366f32bb9cf66434b49a1a5de72a20d91d3278ef3b54046425","last_reissued_at":"2026-07-05T07:55:15.562827Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:55:15.562827Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Brett Mullins, Daniel Sheldon, Gerome Miklau, Miguel Fuentes, Ryan McKenna","submitted_at":"2024-03-12T16:34:07Z","abstract_excerpt":"Mechanisms for generating differentially private synthetic data based on marginals and graphical models have been successful in a wide range of settings. However, one limitation of these methods is their inability to incorporate public data. Initializing a data generating model by pre-training on public data has shown to improve the quality of synthetic data, but this technique is not applicable when model structure is not determined a priori. We develop the mechanism jam-pgm, which expands the adaptive measurements framework to jointly select between measuring public data and private data. Th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.07797","kind":"arxiv","version":1},"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/2403.07797/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":"2403.07797","created_at":"2026-07-05T07:55:15.562888+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.07797v1","created_at":"2026-07-05T07:55:15.562888+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.07797","created_at":"2026-07-05T07:55:15.562888+00:00"},{"alias_kind":"pith_short_12","alias_value":"HVNBZQW55WZT","created_at":"2026-07-05T07:55:15.562888+00:00"},{"alias_kind":"pith_short_16","alias_value":"HVNBZQW55WZTM3ZS","created_at":"2026-07-05T07:55:15.562888+00:00"},{"alias_kind":"pith_short_8","alias_value":"HVNBZQW5","created_at":"2026-07-05T07:55:15.562888+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/HVNBZQW55WZTM3ZSXOOPMZBUWS","json":"https://pith.science/pith/HVNBZQW55WZTM3ZSXOOPMZBUWS.json","graph_json":"https://pith.science/api/pith-number/HVNBZQW55WZTM3ZSXOOPMZBUWS/graph.json","events_json":"https://pith.science/api/pith-number/HVNBZQW55WZTM3ZSXOOPMZBUWS/events.json","paper":"https://pith.science/paper/HVNBZQW5"},"agent_actions":{"view_html":"https://pith.science/pith/HVNBZQW55WZTM3ZSXOOPMZBUWS","download_json":"https://pith.science/pith/HVNBZQW55WZTM3ZSXOOPMZBUWS.json","view_paper":"https://pith.science/paper/HVNBZQW5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.07797&json=true","fetch_graph":"https://pith.science/api/pith-number/HVNBZQW55WZTM3ZSXOOPMZBUWS/graph.json","fetch_events":"https://pith.science/api/pith-number/HVNBZQW55WZTM3ZSXOOPMZBUWS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HVNBZQW55WZTM3ZSXOOPMZBUWS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HVNBZQW55WZTM3ZSXOOPMZBUWS/action/storage_attestation","attest_author":"https://pith.science/pith/HVNBZQW55WZTM3ZSXOOPMZBUWS/action/author_attestation","sign_citation":"https://pith.science/pith/HVNBZQW55WZTM3ZSXOOPMZBUWS/action/citation_signature","submit_replication":"https://pith.science/pith/HVNBZQW55WZTM3ZSXOOPMZBUWS/action/replication_record"}},"created_at":"2026-07-05T07:55:15.562888+00:00","updated_at":"2026-07-05T07:55:15.562888+00:00"}