{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:LDJS3UEA7A6D6626OOS52WKRVS","short_pith_number":"pith:LDJS3UEA","canonical_record":{"source":{"id":"2406.11206","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-17T04:53:47Z","cross_cats_sorted":["cs.CR","stat.ML"],"title_canon_sha256":"2a628c9e5a8e393b32232a5b08194c8219a6e61fa15b84d17b29757319df05f9","abstract_canon_sha256":"8e44ef9254cbeae12471674ca62cfd38cd4eede4638d88dcb246d3e95d9e8406"},"schema_version":"1.0"},"canonical_sha256":"58d32dd080f83c3f7b5e73a5dd5951acbd15776de90a9d96e344331e7eed0b20","source":{"kind":"arxiv","id":"2406.11206","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.11206","created_at":"2026-07-05T10:59:58Z"},{"alias_kind":"arxiv_version","alias_value":"2406.11206v3","created_at":"2026-07-05T10:59:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.11206","created_at":"2026-07-05T10:59:58Z"},{"alias_kind":"pith_short_12","alias_value":"LDJS3UEA7A6D","created_at":"2026-07-05T10:59:58Z"},{"alias_kind":"pith_short_16","alias_value":"LDJS3UEA7A6D6626","created_at":"2026-07-05T10:59:58Z"},{"alias_kind":"pith_short_8","alias_value":"LDJS3UEA","created_at":"2026-07-05T10:59:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:LDJS3UEA7A6D6626OOS52WKRVS","target":"record","payload":{"canonical_record":{"source":{"id":"2406.11206","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-17T04:53:47Z","cross_cats_sorted":["cs.CR","stat.ML"],"title_canon_sha256":"2a628c9e5a8e393b32232a5b08194c8219a6e61fa15b84d17b29757319df05f9","abstract_canon_sha256":"8e44ef9254cbeae12471674ca62cfd38cd4eede4638d88dcb246d3e95d9e8406"},"schema_version":"1.0"},"canonical_sha256":"58d32dd080f83c3f7b5e73a5dd5951acbd15776de90a9d96e344331e7eed0b20","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:59:58.998745Z","signature_b64":"pvPQ36i9xtg23vZoYGlNU+L7d5YZs2yAqV1dXqM40KEaV4dlH/BluLqmJNcglAYKzHxM8ZrdKABjyNKks+JCAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"58d32dd080f83c3f7b5e73a5dd5951acbd15776de90a9d96e344331e7eed0b20","last_reissued_at":"2026-07-05T10:59:58.998210Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:59:58.998210Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.11206","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:59:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ckze0lWaxs0X4SrPD33waC4ZKzdOSxB5nBzpZAhbeaIvbaUFKIoh3ddcrwsJfUZAtzX79WpKTcJGNmWg4lFVBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T09:06:43.517824Z"},"content_sha256":"b999b82debb8d0f94b90f2ca4a0136b3cf2d137cf812b62261376863e936cc9a","schema_version":"1.0","event_id":"sha256:b999b82debb8d0f94b90f2ca4a0136b3cf2d137cf812b62261376863e936cc9a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:LDJS3UEA7A6D6626OOS52WKRVS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Retraining with Predicted Hard Labels Provably Increases Model Accuracy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR","stat.ML"],"primary_cat":"cs.LG","authors_text":"Adel Javanmard, Alessandro Epasto, Inderjit S. Dhillon, Jieming Mao, Peilin Zhong, Rudrajit Das, Sujay Sanghavi, Vahab Mirrokni","submitted_at":"2024-06-17T04:53:47Z","abstract_excerpt":"The performance of a model trained with noisy labels is often improved by simply \\textit{retraining} the model with its \\textit{own predicted hard labels} (i.e., 1/0 labels). Yet, a detailed theoretical characterization of this phenomenon is lacking. In this paper, we theoretically analyze retraining in a linearly separable binary classification setting with randomly corrupted labels given to us and prove that retraining can improve the population accuracy obtained by initially training with the given (noisy) labels. To the best of our knowledge, this is the first such theoretical result. Retr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.11206","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/2406.11206/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":"2026-07-05T10:59:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QrL35uGjoz9n8XmgZKZndSEta7aaXvN/m54dSDYKUGCWoN81EKs2rROvb+1/4KUhyjqnTkV6DFL/EJJtddvIBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T09:06:43.518324Z"},"content_sha256":"b1dafb48bacff1d61b756125c534a1f1dc1fe3c532a9010d6d29c436e10ca1aa","schema_version":"1.0","event_id":"sha256:b1dafb48bacff1d61b756125c534a1f1dc1fe3c532a9010d6d29c436e10ca1aa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LDJS3UEA7A6D6626OOS52WKRVS/bundle.json","state_url":"https://pith.science/pith/LDJS3UEA7A6D6626OOS52WKRVS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LDJS3UEA7A6D6626OOS52WKRVS/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":"2026-08-09T09:06:43Z","links":{"resolver":"https://pith.science/pith/LDJS3UEA7A6D6626OOS52WKRVS","bundle":"https://pith.science/pith/LDJS3UEA7A6D6626OOS52WKRVS/bundle.json","state":"https://pith.science/pith/LDJS3UEA7A6D6626OOS52WKRVS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LDJS3UEA7A6D6626OOS52WKRVS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:LDJS3UEA7A6D6626OOS52WKRVS","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":"8e44ef9254cbeae12471674ca62cfd38cd4eede4638d88dcb246d3e95d9e8406","cross_cats_sorted":["cs.CR","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-17T04:53:47Z","title_canon_sha256":"2a628c9e5a8e393b32232a5b08194c8219a6e61fa15b84d17b29757319df05f9"},"schema_version":"1.0","source":{"id":"2406.11206","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.11206","created_at":"2026-07-05T10:59:58Z"},{"alias_kind":"arxiv_version","alias_value":"2406.11206v3","created_at":"2026-07-05T10:59:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.11206","created_at":"2026-07-05T10:59:58Z"},{"alias_kind":"pith_short_12","alias_value":"LDJS3UEA7A6D","created_at":"2026-07-05T10:59:58Z"},{"alias_kind":"pith_short_16","alias_value":"LDJS3UEA7A6D6626","created_at":"2026-07-05T10:59:58Z"},{"alias_kind":"pith_short_8","alias_value":"LDJS3UEA","created_at":"2026-07-05T10:59:58Z"}],"graph_snapshots":[{"event_id":"sha256:b1dafb48bacff1d61b756125c534a1f1dc1fe3c532a9010d6d29c436e10ca1aa","target":"graph","created_at":"2026-07-05T10:59:58Z","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/2406.11206/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The performance of a model trained with noisy labels is often improved by simply \\textit{retraining} the model with its \\textit{own predicted hard labels} (i.e., 1/0 labels). Yet, a detailed theoretical characterization of this phenomenon is lacking. In this paper, we theoretically analyze retraining in a linearly separable binary classification setting with randomly corrupted labels given to us and prove that retraining can improve the population accuracy obtained by initially training with the given (noisy) labels. To the best of our knowledge, this is the first such theoretical result. Retr","authors_text":"Adel Javanmard, Alessandro Epasto, Inderjit S. Dhillon, Jieming Mao, Peilin Zhong, Rudrajit Das, Sujay Sanghavi, Vahab Mirrokni","cross_cats":["cs.CR","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-17T04:53:47Z","title":"Retraining with Predicted Hard Labels Provably Increases Model Accuracy"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.11206","kind":"arxiv","version":3},"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:b999b82debb8d0f94b90f2ca4a0136b3cf2d137cf812b62261376863e936cc9a","target":"record","created_at":"2026-07-05T10:59:58Z","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":"8e44ef9254cbeae12471674ca62cfd38cd4eede4638d88dcb246d3e95d9e8406","cross_cats_sorted":["cs.CR","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-17T04:53:47Z","title_canon_sha256":"2a628c9e5a8e393b32232a5b08194c8219a6e61fa15b84d17b29757319df05f9"},"schema_version":"1.0","source":{"id":"2406.11206","kind":"arxiv","version":3}},"canonical_sha256":"58d32dd080f83c3f7b5e73a5dd5951acbd15776de90a9d96e344331e7eed0b20","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"58d32dd080f83c3f7b5e73a5dd5951acbd15776de90a9d96e344331e7eed0b20","first_computed_at":"2026-07-05T10:59:58.998210Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:59:58.998210Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pvPQ36i9xtg23vZoYGlNU+L7d5YZs2yAqV1dXqM40KEaV4dlH/BluLqmJNcglAYKzHxM8ZrdKABjyNKks+JCAA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:59:58.998745Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.11206","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b999b82debb8d0f94b90f2ca4a0136b3cf2d137cf812b62261376863e936cc9a","sha256:b1dafb48bacff1d61b756125c534a1f1dc1fe3c532a9010d6d29c436e10ca1aa"],"state_sha256":"733c1a99384c0ae09cdf1f2cf60651525b074aab952a85459cf0eee5d1fddb10"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JqTaID9T/QfPK6qhTjX6i4pSJaWXWox8BYSaSP5h78tYnLNXmtxPO0cbA+k6Nx+z5mHRM4eop/jaqfyTTtFqDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T09:06:43.521842Z","bundle_sha256":"c9dd6a47cbbd6089306168f85539922004f64b3247f6aaef765dfc5ea83949ee"}}