{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:EAXUPAV5Z4Z5B643CK7LUNQB4M","short_pith_number":"pith:EAXUPAV5","schema_version":"1.0","canonical_sha256":"202f4782bdcf33d0fb9b12beba3601e30c1547bb234e4c8f043f95a4190d6e55","source":{"kind":"arxiv","id":"2006.11006","version":1},"attestation_state":"computed","paper":{"title":"Statistical and Algorithmic Insights for Semi-supervised Learning with Self-training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Samet Oymak, Talha Cihad Gulcu","submitted_at":"2020-06-19T08:09:07Z","abstract_excerpt":"Self-training is a classical approach in semi-supervised learning which is successfully applied to a variety of machine learning problems. Self-training algorithm generates pseudo-labels for the unlabeled examples and progressively refines these pseudo-labels which hopefully coincides with the actual labels. This work provides theoretical insights into self-training algorithm with a focus on linear classifiers. We first investigate Gaussian mixture models and provide a sharp non-asymptotic finite-sample characterization of the self-training iterations. Our analysis reveals the provable benefit"},"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":"2006.11006","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-19T08:09:07Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"7ebd7be750768e1477cddff1e04312324a9519efcff6a0ded37f1fc2cc89b397","abstract_canon_sha256":"54d6b75f37358275d0a4bc2bb19bfae199ac20a2a474c3b58aa8c846b8c2394b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:11:27.766946Z","signature_b64":"t7yWFmCigvQY4RceaPfJ8eB32fRNiwBOWKUGf20TxitMUNctkhW1tbPD6Chl8i1qAFdPO7wLb2fUI1UqvgepBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"202f4782bdcf33d0fb9b12beba3601e30c1547bb234e4c8f043f95a4190d6e55","last_reissued_at":"2026-07-05T01:11:27.766423Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:11:27.766423Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Statistical and Algorithmic Insights for Semi-supervised Learning with Self-training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Samet Oymak, Talha Cihad Gulcu","submitted_at":"2020-06-19T08:09:07Z","abstract_excerpt":"Self-training is a classical approach in semi-supervised learning which is successfully applied to a variety of machine learning problems. Self-training algorithm generates pseudo-labels for the unlabeled examples and progressively refines these pseudo-labels which hopefully coincides with the actual labels. This work provides theoretical insights into self-training algorithm with a focus on linear classifiers. We first investigate Gaussian mixture models and provide a sharp non-asymptotic finite-sample characterization of the self-training iterations. Our analysis reveals the provable benefit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.11006","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/2006.11006/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":"2006.11006","created_at":"2026-07-05T01:11:27.766478+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.11006v1","created_at":"2026-07-05T01:11:27.766478+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.11006","created_at":"2026-07-05T01:11:27.766478+00:00"},{"alias_kind":"pith_short_12","alias_value":"EAXUPAV5Z4Z5","created_at":"2026-07-05T01:11:27.766478+00:00"},{"alias_kind":"pith_short_16","alias_value":"EAXUPAV5Z4Z5B643","created_at":"2026-07-05T01:11:27.766478+00:00"},{"alias_kind":"pith_short_8","alias_value":"EAXUPAV5","created_at":"2026-07-05T01:11:27.766478+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.20742","citing_title":"VBFDD-Agent for Electric Vehicle Battery Fault Detection and Diagnosis: Descriptive Text Modeling of Battery Digital Signals","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17778","citing_title":"Self-Distillation is Optimal Among Spectral Shrinkage Estimators in Spiked Covariance Models","ref_index":64,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EAXUPAV5Z4Z5B643CK7LUNQB4M","json":"https://pith.science/pith/EAXUPAV5Z4Z5B643CK7LUNQB4M.json","graph_json":"https://pith.science/api/pith-number/EAXUPAV5Z4Z5B643CK7LUNQB4M/graph.json","events_json":"https://pith.science/api/pith-number/EAXUPAV5Z4Z5B643CK7LUNQB4M/events.json","paper":"https://pith.science/paper/EAXUPAV5"},"agent_actions":{"view_html":"https://pith.science/pith/EAXUPAV5Z4Z5B643CK7LUNQB4M","download_json":"https://pith.science/pith/EAXUPAV5Z4Z5B643CK7LUNQB4M.json","view_paper":"https://pith.science/paper/EAXUPAV5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.11006&json=true","fetch_graph":"https://pith.science/api/pith-number/EAXUPAV5Z4Z5B643CK7LUNQB4M/graph.json","fetch_events":"https://pith.science/api/pith-number/EAXUPAV5Z4Z5B643CK7LUNQB4M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EAXUPAV5Z4Z5B643CK7LUNQB4M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EAXUPAV5Z4Z5B643CK7LUNQB4M/action/storage_attestation","attest_author":"https://pith.science/pith/EAXUPAV5Z4Z5B643CK7LUNQB4M/action/author_attestation","sign_citation":"https://pith.science/pith/EAXUPAV5Z4Z5B643CK7LUNQB4M/action/citation_signature","submit_replication":"https://pith.science/pith/EAXUPAV5Z4Z5B643CK7LUNQB4M/action/replication_record"}},"created_at":"2026-07-05T01:11:27.766478+00:00","updated_at":"2026-07-05T01:11:27.766478+00:00"}