{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:PRH2AXSIHCDLQ2ZJQHCKFVCUET","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":"b3df6fdc5bce7fc9c99854dcd68edb577013a5fe8e8a7344d69d8f35cffaed46","cross_cats_sorted":["cs.AI","math.ST","stat.ME","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-22T18:37:23Z","title_canon_sha256":"ca1b5bd876ee16e9a42414976b7e87e72d1dd180916d6c24f37f61afc8468c14"},"schema_version":"1.0","source":{"id":"2202.11091","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.11091","created_at":"2026-07-05T04:27:11Z"},{"alias_kind":"arxiv_version","alias_value":"2202.11091v2","created_at":"2026-07-05T04:27:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.11091","created_at":"2026-07-05T04:27:11Z"},{"alias_kind":"pith_short_12","alias_value":"PRH2AXSIHCDL","created_at":"2026-07-05T04:27:11Z"},{"alias_kind":"pith_short_16","alias_value":"PRH2AXSIHCDLQ2ZJ","created_at":"2026-07-05T04:27:11Z"},{"alias_kind":"pith_short_8","alias_value":"PRH2AXSI","created_at":"2026-07-05T04:27:11Z"}],"graph_snapshots":[{"event_id":"sha256:a0621c4bd813d96b7116752e2247024d31b13915700a02381adbfb9fe1bf2806","target":"graph","created_at":"2026-07-05T04:27:11Z","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/2202.11091/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Caiming Xiong, Huan Wang, Song Mei, Yingbo Zhou, Yu Bai","cross_cats":["cs.AI","math.ST","stat.ME","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-22T18:37:23Z","title":"Efficient and Differentiable Conformal Prediction with General Function Classes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.11091","kind":"arxiv","version":2},"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":"sha256:1823a499bd1ee06c4288a4fce832f5fe117256aae3c0651ff3d46e3b729efb1f","target":"record","created_at":"2026-07-05T04:27:11Z","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":"b3df6fdc5bce7fc9c99854dcd68edb577013a5fe8e8a7344d69d8f35cffaed46","cross_cats_sorted":["cs.AI","math.ST","stat.ME","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-22T18:37:23Z","title_canon_sha256":"ca1b5bd876ee16e9a42414976b7e87e72d1dd180916d6c24f37f61afc8468c14"},"schema_version":"1.0","source":{"id":"2202.11091","kind":"arxiv","version":2}},"canonical_sha256":"7c4fa05e483886b86b2981c4a2d45424f4746a06c64ac50ae66185f0f9b07f20","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7c4fa05e483886b86b2981c4a2d45424f4746a06c64ac50ae66185f0f9b07f20","first_computed_at":"2026-07-05T04:27:11.621420Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:27:11.621420Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CGO6M7u2VVYx8cA8ZWa/SgIpCBkd10skXnhp2zi5xd1Hhj1foVaW6ehf2ttO3NclCaHj9Qm2s8O0ho4C4UkfDA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:27:11.621826Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.11091","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1823a499bd1ee06c4288a4fce832f5fe117256aae3c0651ff3d46e3b729efb1f","sha256:a0621c4bd813d96b7116752e2247024d31b13915700a02381adbfb9fe1bf2806"],"state_sha256":"47c84ba6586ec30b687e820473609d5a627d45bc01e53f5aaf577009e366c8c6"}