{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HDTOT36GY5V2XB6CJJUZ5MGKAP","short_pith_number":"pith:HDTOT36G","schema_version":"1.0","canonical_sha256":"38e6e9efc6c76bab87c24a699eb0ca03fceabd315516bf924182582eafeec9df","source":{"kind":"arxiv","id":"2402.16300","version":3},"attestation_state":"computed","paper":{"title":"Conformalized Selective Regression","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Anna Sokol, Nitesh Chawla, Nuno Moniz","submitted_at":"2024-02-26T04:43:50Z","abstract_excerpt":"Should prediction models always deliver a prediction? In the pursuit of maximum predictive performance, critical considerations of reliability and fairness are often overshadowed, particularly when it comes to the role of uncertainty. Selective regression, also known as the \"reject option,\" allows models to abstain from predictions in cases of considerable uncertainty. Initially proposed seven decades ago, approaches to selective regression have mostly focused on distribution-based proxies for measuring uncertainty, particularly conditional variance. However, this focus neglects the significan"},"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":"2402.16300","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-26T04:43:50Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"1dce13ce4be700dfa0fe57d6cbe4366238a8c3b9e162e71a075f442ac1873703","abstract_canon_sha256":"c4c9eb1436b81fd2809e31b0010c02f4b967fd34aa818a1e58820fcc9fa380fb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:26:58.396286Z","signature_b64":"p3BoXb9BciB8blRTyvxmfyNIZlttGTwUg4a1jCZDw++mQ+7J0atq4j55YA0wh7ZHTOmMOzPnY3Xw9+jI2UseCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38e6e9efc6c76bab87c24a699eb0ca03fceabd315516bf924182582eafeec9df","last_reissued_at":"2026-07-05T09:26:58.395689Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:26:58.395689Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Conformalized Selective Regression","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Anna Sokol, Nitesh Chawla, Nuno Moniz","submitted_at":"2024-02-26T04:43:50Z","abstract_excerpt":"Should prediction models always deliver a prediction? In the pursuit of maximum predictive performance, critical considerations of reliability and fairness are often overshadowed, particularly when it comes to the role of uncertainty. Selective regression, also known as the \"reject option,\" allows models to abstain from predictions in cases of considerable uncertainty. Initially proposed seven decades ago, approaches to selective regression have mostly focused on distribution-based proxies for measuring uncertainty, particularly conditional variance. However, this focus neglects the significan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.16300","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/2402.16300/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":"2402.16300","created_at":"2026-07-05T09:26:58.395749+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.16300v3","created_at":"2026-07-05T09:26:58.395749+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.16300","created_at":"2026-07-05T09:26:58.395749+00:00"},{"alias_kind":"pith_short_12","alias_value":"HDTOT36GY5V2","created_at":"2026-07-05T09:26:58.395749+00:00"},{"alias_kind":"pith_short_16","alias_value":"HDTOT36GY5V2XB6C","created_at":"2026-07-05T09:26:58.395749+00:00"},{"alias_kind":"pith_short_8","alias_value":"HDTOT36G","created_at":"2026-07-05T09:26:58.395749+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.00506","citing_title":"EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction","ref_index":43,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HDTOT36GY5V2XB6CJJUZ5MGKAP","json":"https://pith.science/pith/HDTOT36GY5V2XB6CJJUZ5MGKAP.json","graph_json":"https://pith.science/api/pith-number/HDTOT36GY5V2XB6CJJUZ5MGKAP/graph.json","events_json":"https://pith.science/api/pith-number/HDTOT36GY5V2XB6CJJUZ5MGKAP/events.json","paper":"https://pith.science/paper/HDTOT36G"},"agent_actions":{"view_html":"https://pith.science/pith/HDTOT36GY5V2XB6CJJUZ5MGKAP","download_json":"https://pith.science/pith/HDTOT36GY5V2XB6CJJUZ5MGKAP.json","view_paper":"https://pith.science/paper/HDTOT36G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.16300&json=true","fetch_graph":"https://pith.science/api/pith-number/HDTOT36GY5V2XB6CJJUZ5MGKAP/graph.json","fetch_events":"https://pith.science/api/pith-number/HDTOT36GY5V2XB6CJJUZ5MGKAP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HDTOT36GY5V2XB6CJJUZ5MGKAP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HDTOT36GY5V2XB6CJJUZ5MGKAP/action/storage_attestation","attest_author":"https://pith.science/pith/HDTOT36GY5V2XB6CJJUZ5MGKAP/action/author_attestation","sign_citation":"https://pith.science/pith/HDTOT36GY5V2XB6CJJUZ5MGKAP/action/citation_signature","submit_replication":"https://pith.science/pith/HDTOT36GY5V2XB6CJJUZ5MGKAP/action/replication_record"}},"created_at":"2026-07-05T09:26:58.395749+00:00","updated_at":"2026-07-05T09:26:58.395749+00:00"}