{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:RWEWA5Q4DMFIQUPYZHEB7425Z6","short_pith_number":"pith:RWEWA5Q4","schema_version":"1.0","canonical_sha256":"8d8960761c1b0a8851f8c9c81ff35dcf97a63128de604a6a299c827534fbabb8","source":{"kind":"arxiv","id":"2106.08864","version":1},"attestation_state":"computed","paper":{"title":"Multi-Class Classification from Single-Class Data with Confidences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bo An, Gang Niu, Lei Feng, Masashi Sugiyama, Senlin Shu, Yitian Xu, Yuzhou Cao","submitted_at":"2021-06-16T15:38:13Z","abstract_excerpt":"Can we learn a multi-class classifier from only data of a single class? We show that without any assumptions on the loss functions, models, and optimizers, we can successfully learn a multi-class classifier from only data of a single class with a rigorous consistency guarantee when confidences (i.e., the class-posterior probabilities for all the classes) are available. Specifically, we propose an empirical risk minimization framework that is loss-/model-/optimizer-independent. Instead of constructing a boundary between the given class and other classes, our method can conduct discriminative cl"},"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":"2106.08864","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-16T15:38:13Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c3f7e2fd6c6fe44b2304d4b17c51b94e1cdb07aca3ade43d891efa590fc822a7","abstract_canon_sha256":"976b63238afea9a0b2eefaa5fab9057f52ca7f13cfbb2167d80ed8116d7b87b7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:50:02.097395Z","signature_b64":"b18erXpOCbNHszi1hhujdtUFcSWdjiAzhldQapSoRxZHlVTZnnyKh/+tpbC9Ra4u641DFZC2DQ/WjJCYe2iaAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d8960761c1b0a8851f8c9c81ff35dcf97a63128de604a6a299c827534fbabb8","last_reissued_at":"2026-07-05T02:50:02.097001Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:50:02.097001Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Class Classification from Single-Class Data with Confidences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bo An, Gang Niu, Lei Feng, Masashi Sugiyama, Senlin Shu, Yitian Xu, Yuzhou Cao","submitted_at":"2021-06-16T15:38:13Z","abstract_excerpt":"Can we learn a multi-class classifier from only data of a single class? We show that without any assumptions on the loss functions, models, and optimizers, we can successfully learn a multi-class classifier from only data of a single class with a rigorous consistency guarantee when confidences (i.e., the class-posterior probabilities for all the classes) are available. Specifically, we propose an empirical risk minimization framework that is loss-/model-/optimizer-independent. Instead of constructing a boundary between the given class and other classes, our method can conduct discriminative cl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.08864","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/2106.08864/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":"2106.08864","created_at":"2026-07-05T02:50:02.097070+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.08864v1","created_at":"2026-07-05T02:50:02.097070+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.08864","created_at":"2026-07-05T02:50:02.097070+00:00"},{"alias_kind":"pith_short_12","alias_value":"RWEWA5Q4DMFI","created_at":"2026-07-05T02:50:02.097070+00:00"},{"alias_kind":"pith_short_16","alias_value":"RWEWA5Q4DMFIQUPY","created_at":"2026-07-05T02:50:02.097070+00:00"},{"alias_kind":"pith_short_8","alias_value":"RWEWA5Q4","created_at":"2026-07-05T02:50:02.097070+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RWEWA5Q4DMFIQUPYZHEB7425Z6","json":"https://pith.science/pith/RWEWA5Q4DMFIQUPYZHEB7425Z6.json","graph_json":"https://pith.science/api/pith-number/RWEWA5Q4DMFIQUPYZHEB7425Z6/graph.json","events_json":"https://pith.science/api/pith-number/RWEWA5Q4DMFIQUPYZHEB7425Z6/events.json","paper":"https://pith.science/paper/RWEWA5Q4"},"agent_actions":{"view_html":"https://pith.science/pith/RWEWA5Q4DMFIQUPYZHEB7425Z6","download_json":"https://pith.science/pith/RWEWA5Q4DMFIQUPYZHEB7425Z6.json","view_paper":"https://pith.science/paper/RWEWA5Q4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.08864&json=true","fetch_graph":"https://pith.science/api/pith-number/RWEWA5Q4DMFIQUPYZHEB7425Z6/graph.json","fetch_events":"https://pith.science/api/pith-number/RWEWA5Q4DMFIQUPYZHEB7425Z6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RWEWA5Q4DMFIQUPYZHEB7425Z6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RWEWA5Q4DMFIQUPYZHEB7425Z6/action/storage_attestation","attest_author":"https://pith.science/pith/RWEWA5Q4DMFIQUPYZHEB7425Z6/action/author_attestation","sign_citation":"https://pith.science/pith/RWEWA5Q4DMFIQUPYZHEB7425Z6/action/citation_signature","submit_replication":"https://pith.science/pith/RWEWA5Q4DMFIQUPYZHEB7425Z6/action/replication_record"}},"created_at":"2026-07-05T02:50:02.097070+00:00","updated_at":"2026-07-05T02:50:02.097070+00:00"}