{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:EHDZGUGVEEZTKB7CN7VRAS6PPX","short_pith_number":"pith:EHDZGUGV","schema_version":"1.0","canonical_sha256":"21c79350d521333507e26feb104bcf7dcbbaea54c026148fd4b08c2fc5bbc1e8","source":{"kind":"arxiv","id":"2501.18084","version":1},"attestation_state":"computed","paper":{"title":"U-aggregation: Unsupervised Aggregation of Multiple Learning Algorithms","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Rui Duan","submitted_at":"2025-01-30T01:42:51Z","abstract_excerpt":"Across various domains, the growing advocacy for open science and open-source machine learning has made an increasing number of models publicly available. These models allow practitioners to integrate them into their own contexts, reducing the need for extensive data labeling, training, and calibration. However, selecting the best model for a specific target population remains challenging due to issues like limited transferability, data heterogeneity, and the difficulty of obtaining true labels or outcomes in real-world settings. In this paper, we propose an unsupervised model aggregation meth"},"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":"2501.18084","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ML","submitted_at":"2025-01-30T01:42:51Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1b022aa55b4270042d3dea56569123aeb97d1995fa9a0bed877adaf950a4b0e5","abstract_canon_sha256":"0aa6719c0b83b75b4a1551fa38e586c56ab4e3efa4f87ea2a2e069b0552ed8b3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:07:06.783337Z","signature_b64":"i2ikvNVxkj1QIXtt1nx1tiRP7wbzhD3UEwnLg2KZ34qZTcs1q1VnBEyuhEk5sZTmLLOSFao51tmnQIninOm7Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"21c79350d521333507e26feb104bcf7dcbbaea54c026148fd4b08c2fc5bbc1e8","last_reissued_at":"2026-07-05T10:07:06.782890Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:07:06.782890Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"U-aggregation: Unsupervised Aggregation of Multiple Learning Algorithms","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Rui Duan","submitted_at":"2025-01-30T01:42:51Z","abstract_excerpt":"Across various domains, the growing advocacy for open science and open-source machine learning has made an increasing number of models publicly available. These models allow practitioners to integrate them into their own contexts, reducing the need for extensive data labeling, training, and calibration. However, selecting the best model for a specific target population remains challenging due to issues like limited transferability, data heterogeneity, and the difficulty of obtaining true labels or outcomes in real-world settings. In this paper, we propose an unsupervised model aggregation meth"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.18084","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/2501.18084/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":"2501.18084","created_at":"2026-07-05T10:07:06.782947+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.18084v1","created_at":"2026-07-05T10:07:06.782947+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.18084","created_at":"2026-07-05T10:07:06.782947+00:00"},{"alias_kind":"pith_short_12","alias_value":"EHDZGUGVEEZT","created_at":"2026-07-05T10:07:06.782947+00:00"},{"alias_kind":"pith_short_16","alias_value":"EHDZGUGVEEZTKB7C","created_at":"2026-07-05T10:07:06.782947+00:00"},{"alias_kind":"pith_short_8","alias_value":"EHDZGUGV","created_at":"2026-07-05T10:07:06.782947+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/EHDZGUGVEEZTKB7CN7VRAS6PPX","json":"https://pith.science/pith/EHDZGUGVEEZTKB7CN7VRAS6PPX.json","graph_json":"https://pith.science/api/pith-number/EHDZGUGVEEZTKB7CN7VRAS6PPX/graph.json","events_json":"https://pith.science/api/pith-number/EHDZGUGVEEZTKB7CN7VRAS6PPX/events.json","paper":"https://pith.science/paper/EHDZGUGV"},"agent_actions":{"view_html":"https://pith.science/pith/EHDZGUGVEEZTKB7CN7VRAS6PPX","download_json":"https://pith.science/pith/EHDZGUGVEEZTKB7CN7VRAS6PPX.json","view_paper":"https://pith.science/paper/EHDZGUGV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.18084&json=true","fetch_graph":"https://pith.science/api/pith-number/EHDZGUGVEEZTKB7CN7VRAS6PPX/graph.json","fetch_events":"https://pith.science/api/pith-number/EHDZGUGVEEZTKB7CN7VRAS6PPX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EHDZGUGVEEZTKB7CN7VRAS6PPX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EHDZGUGVEEZTKB7CN7VRAS6PPX/action/storage_attestation","attest_author":"https://pith.science/pith/EHDZGUGVEEZTKB7CN7VRAS6PPX/action/author_attestation","sign_citation":"https://pith.science/pith/EHDZGUGVEEZTKB7CN7VRAS6PPX/action/citation_signature","submit_replication":"https://pith.science/pith/EHDZGUGVEEZTKB7CN7VRAS6PPX/action/replication_record"}},"created_at":"2026-07-05T10:07:06.782947+00:00","updated_at":"2026-07-05T10:07:06.782947+00:00"}