{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:D3TUSIIKZYN3LL7BXWZTN7476V","short_pith_number":"pith:D3TUSIIK","schema_version":"1.0","canonical_sha256":"1ee749210ace1bb5afe1bdb336ff9ff574acf241a855b5538cdd005008bad683","source":{"kind":"arxiv","id":"1806.00548","version":4},"attestation_state":"computed","paper":{"title":"A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Arshdeep Sekhon, Beilun Wang, Yanjun Qi","submitted_at":"2018-06-01T21:41:34Z","abstract_excerpt":"We consider the problem of including additional knowledge in estimating sparse Gaussian graphical models (sGGMs) from aggregated samples, arising often in bioinformatics and neuroimaging applications. Previous joint sGGM estimators either fail to use existing knowledge or cannot scale-up to many tasks (large $K$) under a high-dimensional (large $p$) situation. In this paper, we propose a novel \\underline{J}oint \\underline{E}lementary \\underline{E}stimator incorporating additional \\underline{K}nowledge (JEEK) to infer multiple related sparse Gaussian Graphical models from large-scale heterogene"},"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":"1806.00548","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-06-01T21:41:34Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"fd2d3cc1007cbb9a941bdc45cb2472dcc29f7b189005cdacd2357cceb1297c29","abstract_canon_sha256":"d5136dc2bd241737cebc7a2bc740804bd218d0fa13a538ad7acc6f676ce553d1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:22.655022Z","signature_b64":"hBJa5cnoREnKxKb2rJhj94/+ZxdyNCgJ9XlnnZdqzP/KCdk8PwNcKfeSnPIiG1+P9rOXvpzFzBW8G2fc52FUBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ee749210ace1bb5afe1bdb336ff9ff574acf241a855b5538cdd005008bad683","last_reissued_at":"2026-05-17T23:48:22.654340Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:22.654340Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Arshdeep Sekhon, Beilun Wang, Yanjun Qi","submitted_at":"2018-06-01T21:41:34Z","abstract_excerpt":"We consider the problem of including additional knowledge in estimating sparse Gaussian graphical models (sGGMs) from aggregated samples, arising often in bioinformatics and neuroimaging applications. Previous joint sGGM estimators either fail to use existing knowledge or cannot scale-up to many tasks (large $K$) under a high-dimensional (large $p$) situation. In this paper, we propose a novel \\underline{J}oint \\underline{E}lementary \\underline{E}stimator incorporating additional \\underline{K}nowledge (JEEK) to infer multiple related sparse Gaussian Graphical models from large-scale heterogene"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.00548","kind":"arxiv","version":4},"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":"1806.00548","created_at":"2026-05-17T23:48:22.654481+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.00548v4","created_at":"2026-05-17T23:48:22.654481+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.00548","created_at":"2026-05-17T23:48:22.654481+00:00"},{"alias_kind":"pith_short_12","alias_value":"D3TUSIIKZYN3","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"D3TUSIIKZYN3LL7B","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"D3TUSIIK","created_at":"2026-05-18T12:32:19.392346+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/D3TUSIIKZYN3LL7BXWZTN7476V","json":"https://pith.science/pith/D3TUSIIKZYN3LL7BXWZTN7476V.json","graph_json":"https://pith.science/api/pith-number/D3TUSIIKZYN3LL7BXWZTN7476V/graph.json","events_json":"https://pith.science/api/pith-number/D3TUSIIKZYN3LL7BXWZTN7476V/events.json","paper":"https://pith.science/paper/D3TUSIIK"},"agent_actions":{"view_html":"https://pith.science/pith/D3TUSIIKZYN3LL7BXWZTN7476V","download_json":"https://pith.science/pith/D3TUSIIKZYN3LL7BXWZTN7476V.json","view_paper":"https://pith.science/paper/D3TUSIIK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.00548&json=true","fetch_graph":"https://pith.science/api/pith-number/D3TUSIIKZYN3LL7BXWZTN7476V/graph.json","fetch_events":"https://pith.science/api/pith-number/D3TUSIIKZYN3LL7BXWZTN7476V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D3TUSIIKZYN3LL7BXWZTN7476V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D3TUSIIKZYN3LL7BXWZTN7476V/action/storage_attestation","attest_author":"https://pith.science/pith/D3TUSIIKZYN3LL7BXWZTN7476V/action/author_attestation","sign_citation":"https://pith.science/pith/D3TUSIIKZYN3LL7BXWZTN7476V/action/citation_signature","submit_replication":"https://pith.science/pith/D3TUSIIKZYN3LL7BXWZTN7476V/action/replication_record"}},"created_at":"2026-05-17T23:48:22.654481+00:00","updated_at":"2026-05-17T23:48:22.654481+00:00"}