{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:S4KDQCODYB4BZ35ZP65HP3VBIU","short_pith_number":"pith:S4KDQCOD","schema_version":"1.0","canonical_sha256":"97143809c3c0781cefb97fba77eea14535bff418eb01ce163e5673913ddb3a6f","source":{"kind":"arxiv","id":"2007.08929","version":2},"attestation_state":"computed","paper":{"title":"Provably Consistent Partial-Label Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bo An, Bo Han, Gang Niu, Jiaqi Lv, Lei Feng, Masashi Sugiyama, Miao Xu, Xin Geng","submitted_at":"2020-07-17T12:19:16Z","abstract_excerpt":"Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels. Even though many practical PLL methods have been proposed in the last two decades, there lacks a theoretical understanding of the consistency of those methods-none of the PLL methods hitherto possesses a generation process of candidate label sets, and then it is still unclear why such a method works on a specific dataset and when it may fail given a different dataset. In this paper, we propose the first generation model of candidate label sets, and dev"},"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":"2007.08929","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-17T12:19:16Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"2fca52643d743484aa8ba1ac44d213f0ae8cdf22a9706b00b537049e96a1f025","abstract_canon_sha256":"d7bff3b9b7d4cfb87012ee037b917b3b5bcfb3bb9ee4a140499d7049a34ce8e5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:45:29.381421Z","signature_b64":"hbML/uQHOyYmxxCQCDjqQkJoVpWOhRBPwa0lWqiHC1uLI2p1QOEBP7awHbPmLGyMzdCfpzzw3O1Ugh+8FOPrAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"97143809c3c0781cefb97fba77eea14535bff418eb01ce163e5673913ddb3a6f","last_reissued_at":"2026-07-05T01:45:29.381004Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:45:29.381004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Provably Consistent Partial-Label Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bo An, Bo Han, Gang Niu, Jiaqi Lv, Lei Feng, Masashi Sugiyama, Miao Xu, Xin Geng","submitted_at":"2020-07-17T12:19:16Z","abstract_excerpt":"Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels. Even though many practical PLL methods have been proposed in the last two decades, there lacks a theoretical understanding of the consistency of those methods-none of the PLL methods hitherto possesses a generation process of candidate label sets, and then it is still unclear why such a method works on a specific dataset and when it may fail given a different dataset. In this paper, we propose the first generation model of candidate label sets, and dev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.08929","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/2007.08929/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":"2007.08929","created_at":"2026-07-05T01:45:29.381061+00:00"},{"alias_kind":"arxiv_version","alias_value":"2007.08929v2","created_at":"2026-07-05T01:45:29.381061+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.08929","created_at":"2026-07-05T01:45:29.381061+00:00"},{"alias_kind":"pith_short_12","alias_value":"S4KDQCODYB4B","created_at":"2026-07-05T01:45:29.381061+00:00"},{"alias_kind":"pith_short_16","alias_value":"S4KDQCODYB4BZ35Z","created_at":"2026-07-05T01:45:29.381061+00:00"},{"alias_kind":"pith_short_8","alias_value":"S4KDQCOD","created_at":"2026-07-05T01:45:29.381061+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/S4KDQCODYB4BZ35ZP65HP3VBIU","json":"https://pith.science/pith/S4KDQCODYB4BZ35ZP65HP3VBIU.json","graph_json":"https://pith.science/api/pith-number/S4KDQCODYB4BZ35ZP65HP3VBIU/graph.json","events_json":"https://pith.science/api/pith-number/S4KDQCODYB4BZ35ZP65HP3VBIU/events.json","paper":"https://pith.science/paper/S4KDQCOD"},"agent_actions":{"view_html":"https://pith.science/pith/S4KDQCODYB4BZ35ZP65HP3VBIU","download_json":"https://pith.science/pith/S4KDQCODYB4BZ35ZP65HP3VBIU.json","view_paper":"https://pith.science/paper/S4KDQCOD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2007.08929&json=true","fetch_graph":"https://pith.science/api/pith-number/S4KDQCODYB4BZ35ZP65HP3VBIU/graph.json","fetch_events":"https://pith.science/api/pith-number/S4KDQCODYB4BZ35ZP65HP3VBIU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S4KDQCODYB4BZ35ZP65HP3VBIU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S4KDQCODYB4BZ35ZP65HP3VBIU/action/storage_attestation","attest_author":"https://pith.science/pith/S4KDQCODYB4BZ35ZP65HP3VBIU/action/author_attestation","sign_citation":"https://pith.science/pith/S4KDQCODYB4BZ35ZP65HP3VBIU/action/citation_signature","submit_replication":"https://pith.science/pith/S4KDQCODYB4BZ35ZP65HP3VBIU/action/replication_record"}},"created_at":"2026-07-05T01:45:29.381061+00:00","updated_at":"2026-07-05T01:45:29.381061+00:00"}