{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JOB5VDYKRBFGDA6455OVMYWP4B","short_pith_number":"pith:JOB5VDYK","schema_version":"1.0","canonical_sha256":"4b83da8f0a884a6183dcef5d5662cfe04ef03fe6e111d5de12ec97856c02f2d7","source":{"kind":"arxiv","id":"2411.01696","version":3},"attestation_state":"computed","paper":{"title":"Conformal Risk Minimization with Variance Reduction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"George J. Pappas, Hamed Hassani, Nicolo Dal Fabbro, Orlando Romero, Sima Noorani","submitted_at":"2024-11-03T21:48:15Z","abstract_excerpt":"Conformal prediction (CP) is a distribution-free framework for achieving probabilistic guarantees on black-box models. CP is generally applied to a model post-training. Recent research efforts, on the other hand, have focused on optimizing CP efficiency during training. We formalize this concept as the problem of conformal risk minimization (CRM). In this direction, conformal training (ConfTr) by Stutz et al.(2022) is a technique that seeks to minimize the expected prediction set size of a model by simulating CP in-between training updates. Despite its potential, we identify a strong source of"},"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":"2411.01696","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-03T21:48:15Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c75a41d8da82efdc0b0744b683dde05f0f38e896449673e41aa410b4b7e8876d","abstract_canon_sha256":"8375e3a80ba4ed0ef29ec8c9a05a0a8785f8b3b461952b16ba0a50feff26b4e6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:11:28.168393Z","signature_b64":"RDfHG/nrf6fdjYuSUAZLQA5qFX5r1G4FpbdX1v5x85PPEuxsvRArlAkbIUnipCdsRBHVz3AMxQPDVhkD9mipAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b83da8f0a884a6183dcef5d5662cfe04ef03fe6e111d5de12ec97856c02f2d7","last_reissued_at":"2026-07-05T10:11:28.167892Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:11:28.167892Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Conformal Risk Minimization with Variance Reduction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"George J. Pappas, Hamed Hassani, Nicolo Dal Fabbro, Orlando Romero, Sima Noorani","submitted_at":"2024-11-03T21:48:15Z","abstract_excerpt":"Conformal prediction (CP) is a distribution-free framework for achieving probabilistic guarantees on black-box models. CP is generally applied to a model post-training. Recent research efforts, on the other hand, have focused on optimizing CP efficiency during training. We formalize this concept as the problem of conformal risk minimization (CRM). In this direction, conformal training (ConfTr) by Stutz et al.(2022) is a technique that seeks to minimize the expected prediction set size of a model by simulating CP in-between training updates. Despite its potential, we identify a strong source of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.01696","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/2411.01696/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":"2411.01696","created_at":"2026-07-05T10:11:28.167953+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.01696v3","created_at":"2026-07-05T10:11:28.167953+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.01696","created_at":"2026-07-05T10:11:28.167953+00:00"},{"alias_kind":"pith_short_12","alias_value":"JOB5VDYKRBFG","created_at":"2026-07-05T10:11:28.167953+00:00"},{"alias_kind":"pith_short_16","alias_value":"JOB5VDYKRBFGDA64","created_at":"2026-07-05T10:11:28.167953+00:00"},{"alias_kind":"pith_short_8","alias_value":"JOB5VDYK","created_at":"2026-07-05T10:11:28.167953+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.05551","citing_title":"Conformal Risk-Averse Decision Making with Action Conditional Guarantee","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JOB5VDYKRBFGDA6455OVMYWP4B","json":"https://pith.science/pith/JOB5VDYKRBFGDA6455OVMYWP4B.json","graph_json":"https://pith.science/api/pith-number/JOB5VDYKRBFGDA6455OVMYWP4B/graph.json","events_json":"https://pith.science/api/pith-number/JOB5VDYKRBFGDA6455OVMYWP4B/events.json","paper":"https://pith.science/paper/JOB5VDYK"},"agent_actions":{"view_html":"https://pith.science/pith/JOB5VDYKRBFGDA6455OVMYWP4B","download_json":"https://pith.science/pith/JOB5VDYKRBFGDA6455OVMYWP4B.json","view_paper":"https://pith.science/paper/JOB5VDYK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.01696&json=true","fetch_graph":"https://pith.science/api/pith-number/JOB5VDYKRBFGDA6455OVMYWP4B/graph.json","fetch_events":"https://pith.science/api/pith-number/JOB5VDYKRBFGDA6455OVMYWP4B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JOB5VDYKRBFGDA6455OVMYWP4B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JOB5VDYKRBFGDA6455OVMYWP4B/action/storage_attestation","attest_author":"https://pith.science/pith/JOB5VDYKRBFGDA6455OVMYWP4B/action/author_attestation","sign_citation":"https://pith.science/pith/JOB5VDYKRBFGDA6455OVMYWP4B/action/citation_signature","submit_replication":"https://pith.science/pith/JOB5VDYKRBFGDA6455OVMYWP4B/action/replication_record"}},"created_at":"2026-07-05T10:11:28.167953+00:00","updated_at":"2026-07-05T10:11:28.167953+00:00"}