{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RWAVVMLBQTLYNHETYYOPZJLNWJ","short_pith_number":"pith:RWAVVMLB","schema_version":"1.0","canonical_sha256":"8d815ab16184d7869c93c61cfca56db255919f89ced0250f0782b3f322c0db06","source":{"kind":"arxiv","id":"2406.07449","version":2},"attestation_state":"computed","paper":{"title":"Boosted Conformal Prediction Intervals","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"Emmanuel J. Cand\\`es, Ran Xie, Rina Foygel Barber","submitted_at":"2024-06-11T16:54:51Z","abstract_excerpt":"This paper introduces a boosted conformal procedure designed to tailor conformalized prediction intervals toward specific desired properties, such as enhanced conditional coverage or reduced interval length. We employ machine learning techniques, notably gradient boosting, to systematically improve upon a predefined conformity score function. This process is guided by carefully constructed loss functions that measure the deviation of prediction intervals from the targeted properties. The procedure operates post-training, relying solely on model predictions and without modifying the trained mod"},"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":"2406.07449","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2024-06-11T16:54:51Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"576f4002fa5e83ed49a7628e4c0c7ecfb191d1447d5f5716eb70a90041ca3117","abstract_canon_sha256":"f00181a3988e146d7ab7150e1a2c983503398fbbbde5dc3cafc7c345ff802f42"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:33:33.022744Z","signature_b64":"fSfoz7nLXeFKcOsJShKFJ8ZMl4L8PWBPqhSWTRI8o2+REkKd2qkQtBjJ+zzQJ0uLBqgZcfRFI2azpd17egdvBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d815ab16184d7869c93c61cfca56db255919f89ced0250f0782b3f322c0db06","last_reissued_at":"2026-07-05T09:33:33.022200Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:33:33.022200Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Boosted Conformal Prediction Intervals","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"Emmanuel J. Cand\\`es, Ran Xie, Rina Foygel Barber","submitted_at":"2024-06-11T16:54:51Z","abstract_excerpt":"This paper introduces a boosted conformal procedure designed to tailor conformalized prediction intervals toward specific desired properties, such as enhanced conditional coverage or reduced interval length. We employ machine learning techniques, notably gradient boosting, to systematically improve upon a predefined conformity score function. This process is guided by carefully constructed loss functions that measure the deviation of prediction intervals from the targeted properties. The procedure operates post-training, relying solely on model predictions and without modifying the trained mod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.07449","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/2406.07449/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":"2406.07449","created_at":"2026-07-05T09:33:33.022254+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.07449v2","created_at":"2026-07-05T09:33:33.022254+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.07449","created_at":"2026-07-05T09:33:33.022254+00:00"},{"alias_kind":"pith_short_12","alias_value":"RWAVVMLBQTLY","created_at":"2026-07-05T09:33:33.022254+00:00"},{"alias_kind":"pith_short_16","alias_value":"RWAVVMLBQTLYNHET","created_at":"2026-07-05T09:33:33.022254+00:00"},{"alias_kind":"pith_short_8","alias_value":"RWAVVMLB","created_at":"2026-07-05T09:33:33.022254+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":20,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RWAVVMLBQTLYNHETYYOPZJLNWJ","json":"https://pith.science/pith/RWAVVMLBQTLYNHETYYOPZJLNWJ.json","graph_json":"https://pith.science/api/pith-number/RWAVVMLBQTLYNHETYYOPZJLNWJ/graph.json","events_json":"https://pith.science/api/pith-number/RWAVVMLBQTLYNHETYYOPZJLNWJ/events.json","paper":"https://pith.science/paper/RWAVVMLB"},"agent_actions":{"view_html":"https://pith.science/pith/RWAVVMLBQTLYNHETYYOPZJLNWJ","download_json":"https://pith.science/pith/RWAVVMLBQTLYNHETYYOPZJLNWJ.json","view_paper":"https://pith.science/paper/RWAVVMLB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.07449&json=true","fetch_graph":"https://pith.science/api/pith-number/RWAVVMLBQTLYNHETYYOPZJLNWJ/graph.json","fetch_events":"https://pith.science/api/pith-number/RWAVVMLBQTLYNHETYYOPZJLNWJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RWAVVMLBQTLYNHETYYOPZJLNWJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RWAVVMLBQTLYNHETYYOPZJLNWJ/action/storage_attestation","attest_author":"https://pith.science/pith/RWAVVMLBQTLYNHETYYOPZJLNWJ/action/author_attestation","sign_citation":"https://pith.science/pith/RWAVVMLBQTLYNHETYYOPZJLNWJ/action/citation_signature","submit_replication":"https://pith.science/pith/RWAVVMLBQTLYNHETYYOPZJLNWJ/action/replication_record"}},"created_at":"2026-07-05T09:33:33.022254+00:00","updated_at":"2026-07-05T09:33:33.022254+00:00"}