{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:L6UNQTFAIC7FBZXWBAP2QHTTZM","short_pith_number":"pith:L6UNQTFA","schema_version":"1.0","canonical_sha256":"5fa8d84ca040be50e6f6081fa81e73cb27666042c59d45cf4df53c1956c23b14","source":{"kind":"arxiv","id":"2501.10139","version":2},"attestation_state":"computed","paper":{"title":"Conformal Prediction Sets with Improved Conditional Coverage using Trust Scores","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","stat.ME","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ahmed Alaa, Jivat Neet Kaur, Michael I. Jordan","submitted_at":"2025-01-17T12:01:56Z","abstract_excerpt":"Standard conformal prediction offers a marginal guarantee on coverage, but for prediction sets to be truly useful, they should ideally ensure coverage conditional on each test point. Unfortunately, it is impossible to achieve exact, distribution-free conditional coverage in finite samples. In this work, we propose an alternative conformal prediction algorithm that targets coverage where it matters most--in instances where a classifier is overconfident in its incorrect predictions. We start by dissecting miscoverage events in marginally-valid conformal prediction, and show that miscoverage rate"},"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":"2501.10139","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-17T12:01:56Z","cross_cats_sorted":["cs.AI","stat.ME","stat.ML"],"title_canon_sha256":"b36fd7f65a92a3a9ecac4fde3ae94e203d0264608cffeeb9353c636aedee9088","abstract_canon_sha256":"cf26ab955277470c30a954b6d97e06b522a1718e58376e89c05eca85d451b59a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:11:31.645781Z","signature_b64":"lNHuflVVpkyEA72VVXuvEAGIV9AKEExDqcp+rnYyI/MQ4ARp/PSew2zcHNyY6MLsCMKmw/OM+bu0imEkWczNBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5fa8d84ca040be50e6f6081fa81e73cb27666042c59d45cf4df53c1956c23b14","last_reissued_at":"2026-07-05T10:11:31.645309Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:11:31.645309Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Conformal Prediction Sets with Improved Conditional Coverage using Trust Scores","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","stat.ME","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ahmed Alaa, Jivat Neet Kaur, Michael I. Jordan","submitted_at":"2025-01-17T12:01:56Z","abstract_excerpt":"Standard conformal prediction offers a marginal guarantee on coverage, but for prediction sets to be truly useful, they should ideally ensure coverage conditional on each test point. Unfortunately, it is impossible to achieve exact, distribution-free conditional coverage in finite samples. In this work, we propose an alternative conformal prediction algorithm that targets coverage where it matters most--in instances where a classifier is overconfident in its incorrect predictions. We start by dissecting miscoverage events in marginally-valid conformal prediction, and show that miscoverage rate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.10139","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/2501.10139/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":"2501.10139","created_at":"2026-07-05T10:11:31.645380+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.10139v2","created_at":"2026-07-05T10:11:31.645380+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.10139","created_at":"2026-07-05T10:11:31.645380+00:00"},{"alias_kind":"pith_short_12","alias_value":"L6UNQTFAIC7F","created_at":"2026-07-05T10:11:31.645380+00:00"},{"alias_kind":"pith_short_16","alias_value":"L6UNQTFAIC7FBZXW","created_at":"2026-07-05T10:11:31.645380+00:00"},{"alias_kind":"pith_short_8","alias_value":"L6UNQTFA","created_at":"2026-07-05T10:11:31.645380+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.29403","citing_title":"Self-Organized Conformal Prediction: Reducing Regional Coverage Gaps with Unsupervised Group Discovery","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06479","citing_title":"Risk-Controlled Post-Processing of Decision Policies","ref_index":131,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L6UNQTFAIC7FBZXWBAP2QHTTZM","json":"https://pith.science/pith/L6UNQTFAIC7FBZXWBAP2QHTTZM.json","graph_json":"https://pith.science/api/pith-number/L6UNQTFAIC7FBZXWBAP2QHTTZM/graph.json","events_json":"https://pith.science/api/pith-number/L6UNQTFAIC7FBZXWBAP2QHTTZM/events.json","paper":"https://pith.science/paper/L6UNQTFA"},"agent_actions":{"view_html":"https://pith.science/pith/L6UNQTFAIC7FBZXWBAP2QHTTZM","download_json":"https://pith.science/pith/L6UNQTFAIC7FBZXWBAP2QHTTZM.json","view_paper":"https://pith.science/paper/L6UNQTFA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.10139&json=true","fetch_graph":"https://pith.science/api/pith-number/L6UNQTFAIC7FBZXWBAP2QHTTZM/graph.json","fetch_events":"https://pith.science/api/pith-number/L6UNQTFAIC7FBZXWBAP2QHTTZM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L6UNQTFAIC7FBZXWBAP2QHTTZM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L6UNQTFAIC7FBZXWBAP2QHTTZM/action/storage_attestation","attest_author":"https://pith.science/pith/L6UNQTFAIC7FBZXWBAP2QHTTZM/action/author_attestation","sign_citation":"https://pith.science/pith/L6UNQTFAIC7FBZXWBAP2QHTTZM/action/citation_signature","submit_replication":"https://pith.science/pith/L6UNQTFAIC7FBZXWBAP2QHTTZM/action/replication_record"}},"created_at":"2026-07-05T10:11:31.645380+00:00","updated_at":"2026-07-05T10:11:31.645380+00:00"}