{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:FFSKMYMNGTFPJ7FOHUMFA7S2BV","short_pith_number":"pith:FFSKMYMN","schema_version":"1.0","canonical_sha256":"2964a6618d34caf4fcae3d18507e5a0d661d8cbe42e35c6469512d8efec030bf","source":{"kind":"arxiv","id":"1209.4129","version":3},"attestation_state":"computed","paper":{"title":"Comunication-Efficient Algorithms for Statistical Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.CO"],"primary_cat":"stat.ML","authors_text":"John C. Duchi, Martin Wainwright, Yuchen Zhang","submitted_at":"2012-09-19T01:27:40Z","abstract_excerpt":"We analyze two communication-efficient algorithms for distributed statistical optimization on large-scale data sets. The first algorithm is a standard averaging method that distributes the $N$ data samples evenly to $\\nummac$ machines, performs separate minimization on each subset, and then averages the estimates. We provide a sharp analysis of this average mixture algorithm, showing that under a reasonable set of conditions, the combined parameter achieves mean-squared error that decays as $\\order(N^{-1}+(N/m)^{-2})$. Whenever $m \\le \\sqrt{N}$, this guarantee matches the best possible rate ac"},"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":"1209.4129","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2012-09-19T01:27:40Z","cross_cats_sorted":["cs.LG","stat.CO"],"title_canon_sha256":"3d3a06ab3cb29facf48fa293a7836f66c117573ef7b153348708fd3972a04927","abstract_canon_sha256":"7be0cedc752b428d9f0aecff5edb8c4f48f2e3a77507e3e2c0443bd1f3069d47"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:10:44.737575Z","signature_b64":"4wP5wsEBXssWW/UbCXdCJbRqCgIPZsXCPik5lDsCpmTwSjppQeaGNZ/ca7XGe8s1FHSlWTfpAce7xk7py1ZmBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2964a6618d34caf4fcae3d18507e5a0d661d8cbe42e35c6469512d8efec030bf","last_reissued_at":"2026-05-18T03:10:44.737085Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:10:44.737085Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Comunication-Efficient Algorithms for Statistical Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.CO"],"primary_cat":"stat.ML","authors_text":"John C. Duchi, Martin Wainwright, Yuchen Zhang","submitted_at":"2012-09-19T01:27:40Z","abstract_excerpt":"We analyze two communication-efficient algorithms for distributed statistical optimization on large-scale data sets. The first algorithm is a standard averaging method that distributes the $N$ data samples evenly to $\\nummac$ machines, performs separate minimization on each subset, and then averages the estimates. We provide a sharp analysis of this average mixture algorithm, showing that under a reasonable set of conditions, the combined parameter achieves mean-squared error that decays as $\\order(N^{-1}+(N/m)^{-2})$. Whenever $m \\le \\sqrt{N}$, this guarantee matches the best possible rate ac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1209.4129","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":"1209.4129","created_at":"2026-05-18T03:10:44.737158+00:00"},{"alias_kind":"arxiv_version","alias_value":"1209.4129v3","created_at":"2026-05-18T03:10:44.737158+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1209.4129","created_at":"2026-05-18T03:10:44.737158+00:00"},{"alias_kind":"pith_short_12","alias_value":"FFSKMYMNGTFP","created_at":"2026-05-18T12:27:06.952714+00:00"},{"alias_kind":"pith_short_16","alias_value":"FFSKMYMNGTFPJ7FO","created_at":"2026-05-18T12:27:06.952714+00:00"},{"alias_kind":"pith_short_8","alias_value":"FFSKMYMN","created_at":"2026-05-18T12:27:06.952714+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/FFSKMYMNGTFPJ7FOHUMFA7S2BV","json":"https://pith.science/pith/FFSKMYMNGTFPJ7FOHUMFA7S2BV.json","graph_json":"https://pith.science/api/pith-number/FFSKMYMNGTFPJ7FOHUMFA7S2BV/graph.json","events_json":"https://pith.science/api/pith-number/FFSKMYMNGTFPJ7FOHUMFA7S2BV/events.json","paper":"https://pith.science/paper/FFSKMYMN"},"agent_actions":{"view_html":"https://pith.science/pith/FFSKMYMNGTFPJ7FOHUMFA7S2BV","download_json":"https://pith.science/pith/FFSKMYMNGTFPJ7FOHUMFA7S2BV.json","view_paper":"https://pith.science/paper/FFSKMYMN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1209.4129&json=true","fetch_graph":"https://pith.science/api/pith-number/FFSKMYMNGTFPJ7FOHUMFA7S2BV/graph.json","fetch_events":"https://pith.science/api/pith-number/FFSKMYMNGTFPJ7FOHUMFA7S2BV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FFSKMYMNGTFPJ7FOHUMFA7S2BV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FFSKMYMNGTFPJ7FOHUMFA7S2BV/action/storage_attestation","attest_author":"https://pith.science/pith/FFSKMYMNGTFPJ7FOHUMFA7S2BV/action/author_attestation","sign_citation":"https://pith.science/pith/FFSKMYMNGTFPJ7FOHUMFA7S2BV/action/citation_signature","submit_replication":"https://pith.science/pith/FFSKMYMNGTFPJ7FOHUMFA7S2BV/action/replication_record"}},"created_at":"2026-05-18T03:10:44.737158+00:00","updated_at":"2026-05-18T03:10:44.737158+00:00"}