{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:NZJNHLKLARCD3H4IEYYENM72RM","short_pith_number":"pith:NZJNHLKL","schema_version":"1.0","canonical_sha256":"6e52d3ad4b04443d9f88263046b3fa8b11b11d314e8ced6cb5bcc7f7d70eaf0d","source":{"kind":"arxiv","id":"1909.07192","version":1},"attestation_state":"computed","paper":{"title":"Learning to Benchmark: Determining Best Achievable Misclassification Error from Training Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alfred Hero, Li Xu, Morteza Noshad","submitted_at":"2019-09-16T13:37:59Z","abstract_excerpt":"We address the problem of learning to benchmark the best achievable classifier performance. In this problem the objective is to establish statistically consistent estimates of the Bayes misclassification error rate without having to learn a Bayes-optimal classifier. Our learning to benchmark framework improves on previous work on learning bounds on Bayes misclassification rate since it learns the {\\it exact} Bayes error rate instead of a bound on error rate. We propose a benchmark learner based on an ensemble of $\\epsilon$-ball estimators and Chebyshev approximation. Under a smoothness assumpt"},"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":"1909.07192","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-09-16T13:37:59Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f9276ef732e5d23e93f3c02e4ea91fb6129b2d7fd67a1a2f139c529128ca33b2","abstract_canon_sha256":"834f4aef10d62f2182eb8f92498e614db93323ac5816f6b850f6189401ca1298"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:04:53.872339Z","signature_b64":"kpogyVqfNZ2QQq/kTPsmppMBdpp0n0R/JD67oqexlUo5X41fGcPdhJJqZtjLXBqNS7tKWr5C9UXrxpzg4ObPCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e52d3ad4b04443d9f88263046b3fa8b11b11d314e8ced6cb5bcc7f7d70eaf0d","last_reissued_at":"2026-07-05T00:04:53.871989Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:04:53.871989Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to Benchmark: Determining Best Achievable Misclassification Error from Training Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alfred Hero, Li Xu, Morteza Noshad","submitted_at":"2019-09-16T13:37:59Z","abstract_excerpt":"We address the problem of learning to benchmark the best achievable classifier performance. In this problem the objective is to establish statistically consistent estimates of the Bayes misclassification error rate without having to learn a Bayes-optimal classifier. Our learning to benchmark framework improves on previous work on learning bounds on Bayes misclassification rate since it learns the {\\it exact} Bayes error rate instead of a bound on error rate. We propose a benchmark learner based on an ensemble of $\\epsilon$-ball estimators and Chebyshev approximation. Under a smoothness assumpt"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.07192","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/1909.07192/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":"1909.07192","created_at":"2026-07-05T00:04:53.872044+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.07192v1","created_at":"2026-07-05T00:04:53.872044+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.07192","created_at":"2026-07-05T00:04:53.872044+00:00"},{"alias_kind":"pith_short_12","alias_value":"NZJNHLKLARCD","created_at":"2026-07-05T00:04:53.872044+00:00"},{"alias_kind":"pith_short_16","alias_value":"NZJNHLKLARCD3H4I","created_at":"2026-07-05T00:04:53.872044+00:00"},{"alias_kind":"pith_short_8","alias_value":"NZJNHLKL","created_at":"2026-07-05T00:04:53.872044+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.07754","citing_title":"Universal Training of Neural Networks to Achieve Bayes Optimal Classification Accuracy","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NZJNHLKLARCD3H4IEYYENM72RM","json":"https://pith.science/pith/NZJNHLKLARCD3H4IEYYENM72RM.json","graph_json":"https://pith.science/api/pith-number/NZJNHLKLARCD3H4IEYYENM72RM/graph.json","events_json":"https://pith.science/api/pith-number/NZJNHLKLARCD3H4IEYYENM72RM/events.json","paper":"https://pith.science/paper/NZJNHLKL"},"agent_actions":{"view_html":"https://pith.science/pith/NZJNHLKLARCD3H4IEYYENM72RM","download_json":"https://pith.science/pith/NZJNHLKLARCD3H4IEYYENM72RM.json","view_paper":"https://pith.science/paper/NZJNHLKL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.07192&json=true","fetch_graph":"https://pith.science/api/pith-number/NZJNHLKLARCD3H4IEYYENM72RM/graph.json","fetch_events":"https://pith.science/api/pith-number/NZJNHLKLARCD3H4IEYYENM72RM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NZJNHLKLARCD3H4IEYYENM72RM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NZJNHLKLARCD3H4IEYYENM72RM/action/storage_attestation","attest_author":"https://pith.science/pith/NZJNHLKLARCD3H4IEYYENM72RM/action/author_attestation","sign_citation":"https://pith.science/pith/NZJNHLKLARCD3H4IEYYENM72RM/action/citation_signature","submit_replication":"https://pith.science/pith/NZJNHLKLARCD3H4IEYYENM72RM/action/replication_record"}},"created_at":"2026-07-05T00:04:53.872044+00:00","updated_at":"2026-07-05T00:04:53.872044+00:00"}