{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:PRH2AXSIHCDLQ2ZJQHCKFVCUET","short_pith_number":"pith:PRH2AXSI","schema_version":"1.0","canonical_sha256":"7c4fa05e483886b86b2981c4a2d45424f4746a06c64ac50ae66185f0f9b07f20","source":{"kind":"arxiv","id":"2202.11091","version":2},"attestation_state":"computed","paper":{"title":"Efficient and Differentiable Conformal Prediction with General Function Classes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","math.ST","stat.ME","stat.ML","stat.TH"],"primary_cat":"cs.LG","authors_text":"Caiming Xiong, Huan Wang, Song Mei, Yingbo Zhou, Yu Bai","submitted_at":"2022-02-22T18:37:23Z","abstract_excerpt":"Quantifying the data uncertainty in learning tasks is often done by learning a prediction interval or prediction set of the label given the input. Two commonly desired properties for learned prediction sets are \\emph{valid coverage} and \\emph{good efficiency} (such as low length or low cardinality). Conformal prediction is a powerful technique for learning prediction sets with valid coverage, yet by default its conformalization step only learns a single parameter, and does not optimize the efficiency over more expressive function classes.\n  In this paper, we propose a generalization of conform"},"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":"2202.11091","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-22T18:37:23Z","cross_cats_sorted":["cs.AI","math.ST","stat.ME","stat.ML","stat.TH"],"title_canon_sha256":"ca1b5bd876ee16e9a42414976b7e87e72d1dd180916d6c24f37f61afc8468c14","abstract_canon_sha256":"b3df6fdc5bce7fc9c99854dcd68edb577013a5fe8e8a7344d69d8f35cffaed46"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:27:11.621826Z","signature_b64":"CGO6M7u2VVYx8cA8ZWa/SgIpCBkd10skXnhp2zi5xd1Hhj1foVaW6ehf2ttO3NclCaHj9Qm2s8O0ho4C4UkfDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c4fa05e483886b86b2981c4a2d45424f4746a06c64ac50ae66185f0f9b07f20","last_reissued_at":"2026-07-05T04:27:11.621420Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:27:11.621420Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient and Differentiable Conformal Prediction with General Function Classes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","math.ST","stat.ME","stat.ML","stat.TH"],"primary_cat":"cs.LG","authors_text":"Caiming Xiong, Huan Wang, Song Mei, Yingbo Zhou, Yu Bai","submitted_at":"2022-02-22T18:37:23Z","abstract_excerpt":"Quantifying the data uncertainty in learning tasks is often done by learning a prediction interval or prediction set of the label given the input. Two commonly desired properties for learned prediction sets are \\emph{valid coverage} and \\emph{good efficiency} (such as low length or low cardinality). Conformal prediction is a powerful technique for learning prediction sets with valid coverage, yet by default its conformalization step only learns a single parameter, and does not optimize the efficiency over more expressive function classes.\n  In this paper, we propose a generalization of conform"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.11091","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/2202.11091/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":"2202.11091","created_at":"2026-07-05T04:27:11.621476+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.11091v2","created_at":"2026-07-05T04:27:11.621476+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.11091","created_at":"2026-07-05T04:27:11.621476+00:00"},{"alias_kind":"pith_short_12","alias_value":"PRH2AXSIHCDL","created_at":"2026-07-05T04:27:11.621476+00:00"},{"alias_kind":"pith_short_16","alias_value":"PRH2AXSIHCDLQ2ZJ","created_at":"2026-07-05T04:27:11.621476+00:00"},{"alias_kind":"pith_short_8","alias_value":"PRH2AXSI","created_at":"2026-07-05T04:27:11.621476+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.08857","citing_title":"RareCP: Regime-Aware Retrieval for Efficient Conformal Prediction","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PRH2AXSIHCDLQ2ZJQHCKFVCUET","json":"https://pith.science/pith/PRH2AXSIHCDLQ2ZJQHCKFVCUET.json","graph_json":"https://pith.science/api/pith-number/PRH2AXSIHCDLQ2ZJQHCKFVCUET/graph.json","events_json":"https://pith.science/api/pith-number/PRH2AXSIHCDLQ2ZJQHCKFVCUET/events.json","paper":"https://pith.science/paper/PRH2AXSI"},"agent_actions":{"view_html":"https://pith.science/pith/PRH2AXSIHCDLQ2ZJQHCKFVCUET","download_json":"https://pith.science/pith/PRH2AXSIHCDLQ2ZJQHCKFVCUET.json","view_paper":"https://pith.science/paper/PRH2AXSI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.11091&json=true","fetch_graph":"https://pith.science/api/pith-number/PRH2AXSIHCDLQ2ZJQHCKFVCUET/graph.json","fetch_events":"https://pith.science/api/pith-number/PRH2AXSIHCDLQ2ZJQHCKFVCUET/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PRH2AXSIHCDLQ2ZJQHCKFVCUET/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PRH2AXSIHCDLQ2ZJQHCKFVCUET/action/storage_attestation","attest_author":"https://pith.science/pith/PRH2AXSIHCDLQ2ZJQHCKFVCUET/action/author_attestation","sign_citation":"https://pith.science/pith/PRH2AXSIHCDLQ2ZJQHCKFVCUET/action/citation_signature","submit_replication":"https://pith.science/pith/PRH2AXSIHCDLQ2ZJQHCKFVCUET/action/replication_record"}},"created_at":"2026-07-05T04:27:11.621476+00:00","updated_at":"2026-07-05T04:27:11.621476+00:00"}