{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:3Y4BSUXYGYWFOSK7U6MX5UYS7R","short_pith_number":"pith:3Y4BSUXY","canonical_record":{"source":{"id":"2006.11488","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-20T04:13:24Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"7e8de830960c207e8ee82ae9cf3acc58e361dec070aa7cca1b2bdb51e5e434c9","abstract_canon_sha256":"ba48c3a60d9f26eb348e57dca591f8ed9dc48393b9fce7df71f99e49f4fddd0b"},"schema_version":"1.0"},"canonical_sha256":"de381952f8362c57495fa7997ed312fc799344e7fd46fc47984bb9e641284f82","source":{"kind":"arxiv","id":"2006.11488","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.11488","created_at":"2026-07-05T01:11:50Z"},{"alias_kind":"arxiv_version","alias_value":"2006.11488v1","created_at":"2026-07-05T01:11:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.11488","created_at":"2026-07-05T01:11:50Z"},{"alias_kind":"pith_short_12","alias_value":"3Y4BSUXYGYWF","created_at":"2026-07-05T01:11:50Z"},{"alias_kind":"pith_short_16","alias_value":"3Y4BSUXYGYWFOSK7","created_at":"2026-07-05T01:11:50Z"},{"alias_kind":"pith_short_8","alias_value":"3Y4BSUXY","created_at":"2026-07-05T01:11:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:3Y4BSUXYGYWFOSK7U6MX5UYS7R","target":"record","payload":{"canonical_record":{"source":{"id":"2006.11488","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-20T04:13:24Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"7e8de830960c207e8ee82ae9cf3acc58e361dec070aa7cca1b2bdb51e5e434c9","abstract_canon_sha256":"ba48c3a60d9f26eb348e57dca591f8ed9dc48393b9fce7df71f99e49f4fddd0b"},"schema_version":"1.0"},"canonical_sha256":"de381952f8362c57495fa7997ed312fc799344e7fd46fc47984bb9e641284f82","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:11:50.674708Z","signature_b64":"xEM0f23CbfCbaarr9m1gA7WgkmNSdnxKnr8YN9auJqJSUYtdCQ1uMK0vPr61rJe+0qTaFCqu6IMRvdwRq+EFCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de381952f8362c57495fa7997ed312fc799344e7fd46fc47984bb9e641284f82","last_reissued_at":"2026-07-05T01:11:50.674296Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:11:50.674296Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2006.11488","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:11:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ITro9TQ2dmCnLkyLdNU7unm9SBX58rPmoHARiZUlTjwKb/o6z3USxb8X+dqM4jFEuW4mN1vgl+aMwBoQhD+vCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T16:57:38.197699Z"},"content_sha256":"f3d5322505610e76dd7298ce4ebe50bfa412d45292608e3310141ec754dd7d84","schema_version":"1.0","event_id":"sha256:f3d5322505610e76dd7298ce4ebe50bfa412d45292608e3310141ec754dd7d84"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:3Y4BSUXYGYWFOSK7U6MX5UYS7R","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Recovering Accurate Labeling Information from Partially Valid Data for Effective Multi-Label Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ximing Li, Yang Wang","submitted_at":"2020-06-20T04:13:24Z","abstract_excerpt":"Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To address the noisy issue, the existing PML methods basically recover the ground-truth labels by leveraging the ground-truth confidence of the candidate label, \\ie the likelihood of a candidate label being a ground-truth one. However, they neglect the information from non-candidate labels, which potentially contributes to the ground-truth label recovery. In this paper, we propose to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.11488","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/2006.11488/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:11:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l7z85K1XuwUATOrJms9bR9nEeCuw8hd7b+LSC6ygVzt0fxD5cdZZa4LYM4ofTiyDUTRYP8ZPd38HiF5MzygDBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T16:57:38.198221Z"},"content_sha256":"a94fc84143e424386b2635e57da17751fea51bc4ed5200487ea3f70e1c195590","schema_version":"1.0","event_id":"sha256:a94fc84143e424386b2635e57da17751fea51bc4ed5200487ea3f70e1c195590"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3Y4BSUXYGYWFOSK7U6MX5UYS7R/bundle.json","state_url":"https://pith.science/pith/3Y4BSUXYGYWFOSK7U6MX5UYS7R/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3Y4BSUXYGYWFOSK7U6MX5UYS7R/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T16:57:38Z","links":{"resolver":"https://pith.science/pith/3Y4BSUXYGYWFOSK7U6MX5UYS7R","bundle":"https://pith.science/pith/3Y4BSUXYGYWFOSK7U6MX5UYS7R/bundle.json","state":"https://pith.science/pith/3Y4BSUXYGYWFOSK7U6MX5UYS7R/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3Y4BSUXYGYWFOSK7U6MX5UYS7R/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:3Y4BSUXYGYWFOSK7U6MX5UYS7R","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ba48c3a60d9f26eb348e57dca591f8ed9dc48393b9fce7df71f99e49f4fddd0b","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-20T04:13:24Z","title_canon_sha256":"7e8de830960c207e8ee82ae9cf3acc58e361dec070aa7cca1b2bdb51e5e434c9"},"schema_version":"1.0","source":{"id":"2006.11488","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.11488","created_at":"2026-07-05T01:11:50Z"},{"alias_kind":"arxiv_version","alias_value":"2006.11488v1","created_at":"2026-07-05T01:11:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.11488","created_at":"2026-07-05T01:11:50Z"},{"alias_kind":"pith_short_12","alias_value":"3Y4BSUXYGYWF","created_at":"2026-07-05T01:11:50Z"},{"alias_kind":"pith_short_16","alias_value":"3Y4BSUXYGYWFOSK7","created_at":"2026-07-05T01:11:50Z"},{"alias_kind":"pith_short_8","alias_value":"3Y4BSUXY","created_at":"2026-07-05T01:11:50Z"}],"graph_snapshots":[{"event_id":"sha256:a94fc84143e424386b2635e57da17751fea51bc4ed5200487ea3f70e1c195590","target":"graph","created_at":"2026-07-05T01:11:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2006.11488/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To address the noisy issue, the existing PML methods basically recover the ground-truth labels by leveraging the ground-truth confidence of the candidate label, \\ie the likelihood of a candidate label being a ground-truth one. However, they neglect the information from non-candidate labels, which potentially contributes to the ground-truth label recovery. In this paper, we propose to ","authors_text":"Ximing Li, Yang Wang","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-20T04:13:24Z","title":"Recovering Accurate Labeling Information from Partially Valid Data for Effective Multi-Label Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.11488","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f3d5322505610e76dd7298ce4ebe50bfa412d45292608e3310141ec754dd7d84","target":"record","created_at":"2026-07-05T01:11:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ba48c3a60d9f26eb348e57dca591f8ed9dc48393b9fce7df71f99e49f4fddd0b","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-20T04:13:24Z","title_canon_sha256":"7e8de830960c207e8ee82ae9cf3acc58e361dec070aa7cca1b2bdb51e5e434c9"},"schema_version":"1.0","source":{"id":"2006.11488","kind":"arxiv","version":1}},"canonical_sha256":"de381952f8362c57495fa7997ed312fc799344e7fd46fc47984bb9e641284f82","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"de381952f8362c57495fa7997ed312fc799344e7fd46fc47984bb9e641284f82","first_computed_at":"2026-07-05T01:11:50.674296Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:11:50.674296Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xEM0f23CbfCbaarr9m1gA7WgkmNSdnxKnr8YN9auJqJSUYtdCQ1uMK0vPr61rJe+0qTaFCqu6IMRvdwRq+EFCA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:11:50.674708Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.11488","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f3d5322505610e76dd7298ce4ebe50bfa412d45292608e3310141ec754dd7d84","sha256:a94fc84143e424386b2635e57da17751fea51bc4ed5200487ea3f70e1c195590"],"state_sha256":"08746678d186cb6eafa6ccb59dd0aa551742f9eccd511c3a93940aaecf6b1bfd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xafDemWaR9d2sMD7NmZLdydmr9xc1G2a2fhF0svN+mkS4XtFBPs4D7wZBDwOVOkVGFvyskmIc7Jhvu6TFjiADQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T16:57:38.202271Z","bundle_sha256":"37985d6b3913eb2d5f787651355dc77fdeaec69d96d7784cde1af8e1282452cc"}}