{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:6VGT5ABUNPYWXGZO44PVSGDSLZ","short_pith_number":"pith:6VGT5ABU","schema_version":"1.0","canonical_sha256":"f54d3e80346bf16b9b2ee71f5918725e7e8373e11f9cc85b27c376b8fe7959a5","source":{"kind":"arxiv","id":"2107.11277","version":3},"attestation_state":"computed","paper":{"title":"Machine Learning with a Reject Option: A survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Dries Van der Plas, Jesse Davis, Kilian Hendrickx, Lorenzo Perini, Wannes Meert","submitted_at":"2021-07-23T14:43:56Z","abstract_excerpt":"Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes can have severe consequences. Albeit already studied in 1970, machine learning with rejection recently gained interest. This machine learning subfield enables machine learning models to abstain from making a prediction when likely to make a mistake.\n  This survey aims to provide an overview on machine learning with rejection. We introduce the conditions leading to two types of rejection, ambiguity and novelty rejection,"},"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":"2107.11277","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-23T14:43:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7e5eeabbcfabc1ba73d7f1ef14ad047ec7fab135ca1c1f8cbb1d564c43d681f3","abstract_canon_sha256":"9fa8d2c3249918e3869325312994e2a5bb7520096c323ccfb51badc2aae0f447"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:47:39.747884Z","signature_b64":"+xui1u4aam7ssxAy0nOXDKcZcFRPcbjhNpgKEWdUCA0XXxI+tMuenWHNAPYLfbHNFiSYWM66bSZqbokYZ6MwBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f54d3e80346bf16b9b2ee71f5918725e7e8373e11f9cc85b27c376b8fe7959a5","last_reissued_at":"2026-07-05T07:47:39.747394Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:47:39.747394Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine Learning with a Reject Option: A survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Dries Van der Plas, Jesse Davis, Kilian Hendrickx, Lorenzo Perini, Wannes Meert","submitted_at":"2021-07-23T14:43:56Z","abstract_excerpt":"Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes can have severe consequences. Albeit already studied in 1970, machine learning with rejection recently gained interest. This machine learning subfield enables machine learning models to abstain from making a prediction when likely to make a mistake.\n  This survey aims to provide an overview on machine learning with rejection. We introduce the conditions leading to two types of rejection, ambiguity and novelty rejection,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.11277","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/2107.11277/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":"2107.11277","created_at":"2026-07-05T07:47:39.747460+00:00"},{"alias_kind":"arxiv_version","alias_value":"2107.11277v3","created_at":"2026-07-05T07:47:39.747460+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.11277","created_at":"2026-07-05T07:47:39.747460+00:00"},{"alias_kind":"pith_short_12","alias_value":"6VGT5ABUNPYW","created_at":"2026-07-05T07:47:39.747460+00:00"},{"alias_kind":"pith_short_16","alias_value":"6VGT5ABUNPYWXGZO","created_at":"2026-07-05T07:47:39.747460+00:00"},{"alias_kind":"pith_short_8","alias_value":"6VGT5ABU","created_at":"2026-07-05T07:47:39.747460+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.31331","citing_title":"Expected Gain-based Escalation in Vertical Federated Learning","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2511.10370","citing_title":"SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation","ref_index":22,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6VGT5ABUNPYWXGZO44PVSGDSLZ","json":"https://pith.science/pith/6VGT5ABUNPYWXGZO44PVSGDSLZ.json","graph_json":"https://pith.science/api/pith-number/6VGT5ABUNPYWXGZO44PVSGDSLZ/graph.json","events_json":"https://pith.science/api/pith-number/6VGT5ABUNPYWXGZO44PVSGDSLZ/events.json","paper":"https://pith.science/paper/6VGT5ABU"},"agent_actions":{"view_html":"https://pith.science/pith/6VGT5ABUNPYWXGZO44PVSGDSLZ","download_json":"https://pith.science/pith/6VGT5ABUNPYWXGZO44PVSGDSLZ.json","view_paper":"https://pith.science/paper/6VGT5ABU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2107.11277&json=true","fetch_graph":"https://pith.science/api/pith-number/6VGT5ABUNPYWXGZO44PVSGDSLZ/graph.json","fetch_events":"https://pith.science/api/pith-number/6VGT5ABUNPYWXGZO44PVSGDSLZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6VGT5ABUNPYWXGZO44PVSGDSLZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6VGT5ABUNPYWXGZO44PVSGDSLZ/action/storage_attestation","attest_author":"https://pith.science/pith/6VGT5ABUNPYWXGZO44PVSGDSLZ/action/author_attestation","sign_citation":"https://pith.science/pith/6VGT5ABUNPYWXGZO44PVSGDSLZ/action/citation_signature","submit_replication":"https://pith.science/pith/6VGT5ABUNPYWXGZO44PVSGDSLZ/action/replication_record"}},"created_at":"2026-07-05T07:47:39.747460+00:00","updated_at":"2026-07-05T07:47:39.747460+00:00"}