{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:RZVXEUN3SLVJT7CRPAWIEVDGCE","short_pith_number":"pith:RZVXEUN3","schema_version":"1.0","canonical_sha256":"8e6b7251bb92ea99fc51782c8254661129156279dd015b8d4cff91939ffe45bb","source":{"kind":"arxiv","id":"1402.1267","version":3},"attestation_state":"computed","paper":{"title":"Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Jiaming Xu, Yudong Chen","submitted_at":"2014-02-06T07:58:38Z","abstract_excerpt":"We consider two closely related problems: planted clustering and submatrix localization. The planted clustering problem assumes that a random graph is generated based on some underlying clusters of the nodes; the task is to recover these clusters given the graph. The submatrix localization problem concerns locating hidden submatrices with elevated means inside a large real-valued random matrix. Of particular interest is the setting where the number of clusters/submatrices is allowed to grow unbounded with the problem size. These formulations cover several classical models such as planted cliqu"},"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":"1402.1267","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-06T07:58:38Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"af803db3e1a605e152ea64fcb23020ee63f6855e98b59b2a4eb8efd893aace66","abstract_canon_sha256":"a4f0b7c36d85641670d9d1fc45d06e757cf05201b23942110de1d915a59c3824"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:23:58.789499Z","signature_b64":"6LGrvwCbvAmqbJOO6ehiFFgmZwhgXNW0SU30Ap6+SrXvBhu46gCxDwyu8yQEcBjZtkKd7ScAv2l0DRaukkPcDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e6b7251bb92ea99fc51782c8254661129156279dd015b8d4cff91939ffe45bb","last_reissued_at":"2026-05-18T02:23:58.788915Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:23:58.788915Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Jiaming Xu, Yudong Chen","submitted_at":"2014-02-06T07:58:38Z","abstract_excerpt":"We consider two closely related problems: planted clustering and submatrix localization. The planted clustering problem assumes that a random graph is generated based on some underlying clusters of the nodes; the task is to recover these clusters given the graph. The submatrix localization problem concerns locating hidden submatrices with elevated means inside a large real-valued random matrix. Of particular interest is the setting where the number of clusters/submatrices is allowed to grow unbounded with the problem size. These formulations cover several classical models such as planted cliqu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.1267","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":""},"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":"1402.1267","created_at":"2026-05-18T02:23:58.789021+00:00"},{"alias_kind":"arxiv_version","alias_value":"1402.1267v3","created_at":"2026-05-18T02:23:58.789021+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1402.1267","created_at":"2026-05-18T02:23:58.789021+00:00"},{"alias_kind":"pith_short_12","alias_value":"RZVXEUN3SLVJ","created_at":"2026-05-18T12:28:46.137349+00:00"},{"alias_kind":"pith_short_16","alias_value":"RZVXEUN3SLVJT7CR","created_at":"2026-05-18T12:28:46.137349+00:00"},{"alias_kind":"pith_short_8","alias_value":"RZVXEUN3","created_at":"2026-05-18T12:28:46.137349+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/RZVXEUN3SLVJT7CRPAWIEVDGCE","json":"https://pith.science/pith/RZVXEUN3SLVJT7CRPAWIEVDGCE.json","graph_json":"https://pith.science/api/pith-number/RZVXEUN3SLVJT7CRPAWIEVDGCE/graph.json","events_json":"https://pith.science/api/pith-number/RZVXEUN3SLVJT7CRPAWIEVDGCE/events.json","paper":"https://pith.science/paper/RZVXEUN3"},"agent_actions":{"view_html":"https://pith.science/pith/RZVXEUN3SLVJT7CRPAWIEVDGCE","download_json":"https://pith.science/pith/RZVXEUN3SLVJT7CRPAWIEVDGCE.json","view_paper":"https://pith.science/paper/RZVXEUN3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1402.1267&json=true","fetch_graph":"https://pith.science/api/pith-number/RZVXEUN3SLVJT7CRPAWIEVDGCE/graph.json","fetch_events":"https://pith.science/api/pith-number/RZVXEUN3SLVJT7CRPAWIEVDGCE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RZVXEUN3SLVJT7CRPAWIEVDGCE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RZVXEUN3SLVJT7CRPAWIEVDGCE/action/storage_attestation","attest_author":"https://pith.science/pith/RZVXEUN3SLVJT7CRPAWIEVDGCE/action/author_attestation","sign_citation":"https://pith.science/pith/RZVXEUN3SLVJT7CRPAWIEVDGCE/action/citation_signature","submit_replication":"https://pith.science/pith/RZVXEUN3SLVJT7CRPAWIEVDGCE/action/replication_record"}},"created_at":"2026-05-18T02:23:58.789021+00:00","updated_at":"2026-05-18T02:23:58.789021+00:00"}