{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KFTHCBCWNFSJKH5UR4NVJF32QM","short_pith_number":"pith:KFTHCBCW","schema_version":"1.0","canonical_sha256":"51667104566964951fb48f1b54977a8312dae8918ae738c30efcfec60ced0588","source":{"kind":"arxiv","id":"2405.00417","version":1},"attestation_state":"computed","paper":{"title":"Conformal Risk Control for Ordinal Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ME","stat.ML"],"primary_cat":"cs.LG","authors_text":"Wenge Guo, Yunpeng Xu, Zhi Wei","submitted_at":"2024-05-01T09:55:31Z","abstract_excerpt":"As a natural extension to the standard conformal prediction method, several conformal risk control methods have been recently developed and applied to various learning problems. In this work, we seek to control the conformal risk in expectation for ordinal classification tasks, which have broad applications to many real problems. For this purpose, we firstly formulated the ordinal classification task in the conformal risk control framework, and provided theoretic risk bounds of the risk control method. Then we proposed two types of loss functions specially designed for ordinal classification t"},"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":"2405.00417","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-01T09:55:31Z","cross_cats_sorted":["stat.ME","stat.ML"],"title_canon_sha256":"b29c8a7cffc5c4584eee87a9e41b1a759055334dadb886458e69c727cb004387","abstract_canon_sha256":"f34339d1669e892729e7e50f49355b6fe1fd1c35bea21c07b67c363ba9ddf9d3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:14:12.661132Z","signature_b64":"l6uKNo8Lg/UaCgHKen54KUO5cbLGJi3foSdej/4jcycSQ1mlxUR+Llan3Am0mkcepqJHDhGQy/+mh6oESRqaDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51667104566964951fb48f1b54977a8312dae8918ae738c30efcfec60ced0588","last_reissued_at":"2026-07-05T08:14:12.660656Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:14:12.660656Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Conformal Risk Control for Ordinal Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ME","stat.ML"],"primary_cat":"cs.LG","authors_text":"Wenge Guo, Yunpeng Xu, Zhi Wei","submitted_at":"2024-05-01T09:55:31Z","abstract_excerpt":"As a natural extension to the standard conformal prediction method, several conformal risk control methods have been recently developed and applied to various learning problems. In this work, we seek to control the conformal risk in expectation for ordinal classification tasks, which have broad applications to many real problems. For this purpose, we firstly formulated the ordinal classification task in the conformal risk control framework, and provided theoretic risk bounds of the risk control method. Then we proposed two types of loss functions specially designed for ordinal classification t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.00417","kind":"arxiv","version":1},"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/2405.00417/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":"2405.00417","created_at":"2026-07-05T08:14:12.660712+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.00417v1","created_at":"2026-07-05T08:14:12.660712+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.00417","created_at":"2026-07-05T08:14:12.660712+00:00"},{"alias_kind":"pith_short_12","alias_value":"KFTHCBCWNFSJ","created_at":"2026-07-05T08:14:12.660712+00:00"},{"alias_kind":"pith_short_16","alias_value":"KFTHCBCWNFSJKH5U","created_at":"2026-07-05T08:14:12.660712+00:00"},{"alias_kind":"pith_short_8","alias_value":"KFTHCBCW","created_at":"2026-07-05T08:14:12.660712+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.08517","citing_title":"A Joint Finite-Sample Certificate for Adaptive Selective Conformal Risk Control","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KFTHCBCWNFSJKH5UR4NVJF32QM","json":"https://pith.science/pith/KFTHCBCWNFSJKH5UR4NVJF32QM.json","graph_json":"https://pith.science/api/pith-number/KFTHCBCWNFSJKH5UR4NVJF32QM/graph.json","events_json":"https://pith.science/api/pith-number/KFTHCBCWNFSJKH5UR4NVJF32QM/events.json","paper":"https://pith.science/paper/KFTHCBCW"},"agent_actions":{"view_html":"https://pith.science/pith/KFTHCBCWNFSJKH5UR4NVJF32QM","download_json":"https://pith.science/pith/KFTHCBCWNFSJKH5UR4NVJF32QM.json","view_paper":"https://pith.science/paper/KFTHCBCW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.00417&json=true","fetch_graph":"https://pith.science/api/pith-number/KFTHCBCWNFSJKH5UR4NVJF32QM/graph.json","fetch_events":"https://pith.science/api/pith-number/KFTHCBCWNFSJKH5UR4NVJF32QM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KFTHCBCWNFSJKH5UR4NVJF32QM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KFTHCBCWNFSJKH5UR4NVJF32QM/action/storage_attestation","attest_author":"https://pith.science/pith/KFTHCBCWNFSJKH5UR4NVJF32QM/action/author_attestation","sign_citation":"https://pith.science/pith/KFTHCBCWNFSJKH5UR4NVJF32QM/action/citation_signature","submit_replication":"https://pith.science/pith/KFTHCBCWNFSJKH5UR4NVJF32QM/action/replication_record"}},"created_at":"2026-07-05T08:14:12.660712+00:00","updated_at":"2026-07-05T08:14:12.660712+00:00"}