{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BLPHAY4JRXTTWIPHO43MCDPLST","short_pith_number":"pith:BLPHAY4J","schema_version":"1.0","canonical_sha256":"0ade7063898de73b21e77736c10deb94f462c208d7faf0f8caeb15b12043933c","source":{"kind":"arxiv","id":"2505.11785","version":3},"attestation_state":"computed","paper":{"title":"Improving Coverage in Combined Prediction Sets with Weighted p-values","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Anqi Liu, Drew Prinster, Gina Wong, Rama Chellappa, Suchi Saria","submitted_at":"2025-05-17T01:51:28Z","abstract_excerpt":"Conformal prediction quantifies the uncertainty of machine learning models by augmenting point predictions with valid prediction sets. For complex scenarios involving multiple trials, models, or data sources, conformal prediction sets can be aggregated to create a prediction set that captures the overall uncertainty, often improving precision. However, aggregating multiple prediction sets with individual $1-\\alpha$ coverage inevitably weakens the overall guarantee, typically resulting in $1-2\\alpha$ worst-case coverage. In this work, we propose a framework for the weighted aggregation of predi"},"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":"2505.11785","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-17T01:51:28Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"cedac7d5ae20afa0b16cc77b9f3b8fcde22d537830f43f387c327a1771e2e46f","abstract_canon_sha256":"bddc3c25dadfd21ef6992e6d6a07f6f1bc6e127f80110e006a374b9e423b8c02"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T02:05:41.013518Z","signature_b64":"1Qx0JSi00OOC4WOAIuMPIFJ7JIB4FNA7mxur/hLacFrv6BcLj4VIel/HE0HFKfO7lS82lyh5PrdBqtvb/sDUDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ade7063898de73b21e77736c10deb94f462c208d7faf0f8caeb15b12043933c","last_reissued_at":"2026-06-03T02:05:41.013024Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T02:05:41.013024Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Coverage in Combined Prediction Sets with Weighted p-values","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Anqi Liu, Drew Prinster, Gina Wong, Rama Chellappa, Suchi Saria","submitted_at":"2025-05-17T01:51:28Z","abstract_excerpt":"Conformal prediction quantifies the uncertainty of machine learning models by augmenting point predictions with valid prediction sets. For complex scenarios involving multiple trials, models, or data sources, conformal prediction sets can be aggregated to create a prediction set that captures the overall uncertainty, often improving precision. However, aggregating multiple prediction sets with individual $1-\\alpha$ coverage inevitably weakens the overall guarantee, typically resulting in $1-2\\alpha$ worst-case coverage. In this work, we propose a framework for the weighted aggregation of predi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.11785","kind":"arxiv","version":3},"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/2505.11785/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":"2505.11785","created_at":"2026-06-03T02:05:41.013085+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.11785v3","created_at":"2026-06-03T02:05:41.013085+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.11785","created_at":"2026-06-03T02:05:41.013085+00:00"},{"alias_kind":"pith_short_12","alias_value":"BLPHAY4JRXTT","created_at":"2026-06-03T02:05:41.013085+00:00"},{"alias_kind":"pith_short_16","alias_value":"BLPHAY4JRXTTWIPH","created_at":"2026-06-03T02:05:41.013085+00:00"},{"alias_kind":"pith_short_8","alias_value":"BLPHAY4J","created_at":"2026-06-03T02:05:41.013085+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/BLPHAY4JRXTTWIPHO43MCDPLST","json":"https://pith.science/pith/BLPHAY4JRXTTWIPHO43MCDPLST.json","graph_json":"https://pith.science/api/pith-number/BLPHAY4JRXTTWIPHO43MCDPLST/graph.json","events_json":"https://pith.science/api/pith-number/BLPHAY4JRXTTWIPHO43MCDPLST/events.json","paper":"https://pith.science/paper/BLPHAY4J"},"agent_actions":{"view_html":"https://pith.science/pith/BLPHAY4JRXTTWIPHO43MCDPLST","download_json":"https://pith.science/pith/BLPHAY4JRXTTWIPHO43MCDPLST.json","view_paper":"https://pith.science/paper/BLPHAY4J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.11785&json=true","fetch_graph":"https://pith.science/api/pith-number/BLPHAY4JRXTTWIPHO43MCDPLST/graph.json","fetch_events":"https://pith.science/api/pith-number/BLPHAY4JRXTTWIPHO43MCDPLST/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BLPHAY4JRXTTWIPHO43MCDPLST/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BLPHAY4JRXTTWIPHO43MCDPLST/action/storage_attestation","attest_author":"https://pith.science/pith/BLPHAY4JRXTTWIPHO43MCDPLST/action/author_attestation","sign_citation":"https://pith.science/pith/BLPHAY4JRXTTWIPHO43MCDPLST/action/citation_signature","submit_replication":"https://pith.science/pith/BLPHAY4JRXTTWIPHO43MCDPLST/action/replication_record"}},"created_at":"2026-06-03T02:05:41.013085+00:00","updated_at":"2026-06-03T02:05:41.013085+00:00"}