{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:7E2XKEHEISEACT7XHPUOYAVCQ2","short_pith_number":"pith:7E2XKEHE","schema_version":"1.0","canonical_sha256":"f9357510e44488014ff73be8ec02a286afec7ce5cf32ef06f99d65d84dbbd149","source":{"kind":"arxiv","id":"2405.02648","version":2},"attestation_state":"computed","paper":{"title":"A Conformal Prediction Score that is Robust to Label Noise","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Coby Penso, Jacob Goldberger","submitted_at":"2024-05-04T12:22:02Z","abstract_excerpt":"Conformal Prediction (CP) quantifies network uncertainty by building a small prediction set with a pre-defined probability that the correct class is within this set. In this study we tackle the problem of CP calibration based on a validation set with noisy labels. We introduce a conformal score that is robust to label noise. The noise-free conformal score is estimated using the noisy labeled data and the noise level. In the test phase the noise-free score is used to form the prediction set. We applied the proposed algorithm to several standard medical imaging classification datasets. We show 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":"2405.02648","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-04T12:22:02Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"65a0ba950551b214422b86600ef22a96cac605a34abda1aea021f83ce1189d83","abstract_canon_sha256":"95481d90ccc66c5fcf12fbfc8175f38e66ebf777e3d233c0897673d52c5d8c78"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:21:15.880493Z","signature_b64":"72lsF6k0R2GZZwXZ43V8sxZij0T9YQQdaKMxFXe08C+ruRJdQcQUK2eCR7spP/C5YT13KkcRnLwt4KdRKwDDBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f9357510e44488014ff73be8ec02a286afec7ce5cf32ef06f99d65d84dbbd149","last_reissued_at":"2026-07-05T08:21:15.880062Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:21:15.880062Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Conformal Prediction Score that is Robust to Label Noise","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Coby Penso, Jacob Goldberger","submitted_at":"2024-05-04T12:22:02Z","abstract_excerpt":"Conformal Prediction (CP) quantifies network uncertainty by building a small prediction set with a pre-defined probability that the correct class is within this set. In this study we tackle the problem of CP calibration based on a validation set with noisy labels. We introduce a conformal score that is robust to label noise. The noise-free conformal score is estimated using the noisy labeled data and the noise level. In the test phase the noise-free score is used to form the prediction set. We applied the proposed algorithm to several standard medical imaging classification datasets. We show t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.02648","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/2405.02648/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":"2405.02648","created_at":"2026-07-05T08:21:15.880121+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.02648v2","created_at":"2026-07-05T08:21:15.880121+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.02648","created_at":"2026-07-05T08:21:15.880121+00:00"},{"alias_kind":"pith_short_12","alias_value":"7E2XKEHEISEA","created_at":"2026-07-05T08:21:15.880121+00:00"},{"alias_kind":"pith_short_16","alias_value":"7E2XKEHEISEACT7X","created_at":"2026-07-05T08:21:15.880121+00:00"},{"alias_kind":"pith_short_8","alias_value":"7E2XKEHE","created_at":"2026-07-05T08:21:15.880121+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.06204","citing_title":"When Does Trimming Help Conformal Prediction? A Retained-Law Diagnostic under Calibration Contamination","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06605","citing_title":"How Many Iterations to Jailbreak? Dynamic Budget Allocation for Multi-Turn LLM Evaluation","ref_index":34,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7E2XKEHEISEACT7XHPUOYAVCQ2","json":"https://pith.science/pith/7E2XKEHEISEACT7XHPUOYAVCQ2.json","graph_json":"https://pith.science/api/pith-number/7E2XKEHEISEACT7XHPUOYAVCQ2/graph.json","events_json":"https://pith.science/api/pith-number/7E2XKEHEISEACT7XHPUOYAVCQ2/events.json","paper":"https://pith.science/paper/7E2XKEHE"},"agent_actions":{"view_html":"https://pith.science/pith/7E2XKEHEISEACT7XHPUOYAVCQ2","download_json":"https://pith.science/pith/7E2XKEHEISEACT7XHPUOYAVCQ2.json","view_paper":"https://pith.science/paper/7E2XKEHE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.02648&json=true","fetch_graph":"https://pith.science/api/pith-number/7E2XKEHEISEACT7XHPUOYAVCQ2/graph.json","fetch_events":"https://pith.science/api/pith-number/7E2XKEHEISEACT7XHPUOYAVCQ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7E2XKEHEISEACT7XHPUOYAVCQ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7E2XKEHEISEACT7XHPUOYAVCQ2/action/storage_attestation","attest_author":"https://pith.science/pith/7E2XKEHEISEACT7XHPUOYAVCQ2/action/author_attestation","sign_citation":"https://pith.science/pith/7E2XKEHEISEACT7XHPUOYAVCQ2/action/citation_signature","submit_replication":"https://pith.science/pith/7E2XKEHEISEACT7XHPUOYAVCQ2/action/replication_record"}},"created_at":"2026-07-05T08:21:15.880121+00:00","updated_at":"2026-07-05T08:21:15.880121+00:00"}