{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:IPDMYQLXIRNWCN42SUDCBVQ53N","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":"b9d6942b5d833e764f311d04374c49a4d48edd36ac6ce7423a1c6f10495540d9","cross_cats_sorted":["cs.DC","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-11-17T15:31:04Z","title_canon_sha256":"13e0b0b8edea78f47e5284a371d8006bc8433471d343d3b118a7487bfe9ca7e4"},"schema_version":"1.0","source":{"id":"1411.4510","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1411.4510","created_at":"2026-05-18T02:35:45Z"},{"alias_kind":"arxiv_version","alias_value":"1411.4510v1","created_at":"2026-05-18T02:35:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1411.4510","created_at":"2026-05-18T02:35:45Z"},{"alias_kind":"pith_short_12","alias_value":"IPDMYQLXIRNW","created_at":"2026-05-18T12:28:33Z"},{"alias_kind":"pith_short_16","alias_value":"IPDMYQLXIRNWCN42","created_at":"2026-05-18T12:28:33Z"},{"alias_kind":"pith_short_8","alias_value":"IPDMYQLX","created_at":"2026-05-18T12:28:33Z"}],"graph_snapshots":[{"event_id":"sha256:4f1f467e491253818ade8ef2f2918a8c89738c2877e1216b1ce5af3daf641d5d","target":"graph","created_at":"2026-05-18T02:35:45Z","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"},"paper":{"abstract_excerpt":"The expressive power of a Gaussian process (GP) model comes at a cost of poor scalability in the data size. To improve its scalability, this paper presents a low-rank-cum-Markov approximation (LMA) of the GP model that is novel in leveraging the dual computational advantages stemming from complementing a low-rank approximate representation of the full-rank GP based on a support set of inputs with a Markov approximation of the resulting residual process; the latter approximation is guaranteed to be closest in the Kullback-Leibler distance criterion subject to some constraint and is considerably","authors_text":"Jiangbo Yu, Jie Chen, Kian Hsiang Low, Patrick Jaillet","cross_cats":["cs.DC","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-11-17T15:31:04Z","title":"Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov Approximation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1411.4510","kind":"arxiv","version":1},"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":"sha256:3b563a4b687fc4414330af85083ed9f28cae6a17b1e3f584530c6cce5423feb3","target":"record","created_at":"2026-05-18T02:35:45Z","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":"b9d6942b5d833e764f311d04374c49a4d48edd36ac6ce7423a1c6f10495540d9","cross_cats_sorted":["cs.DC","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-11-17T15:31:04Z","title_canon_sha256":"13e0b0b8edea78f47e5284a371d8006bc8433471d343d3b118a7487bfe9ca7e4"},"schema_version":"1.0","source":{"id":"1411.4510","kind":"arxiv","version":1}},"canonical_sha256":"43c6cc4177445b61379a950620d61ddb4868c1222cfd095b5c435d1b46b5faac","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"43c6cc4177445b61379a950620d61ddb4868c1222cfd095b5c435d1b46b5faac","first_computed_at":"2026-05-18T02:35:45.609469Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:35:45.609469Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pxmQ+nEfzVW/QEfQqpvvKQRX+cDMXvnjM/Qey2z0fGLucLUAzCbQbzw61qCC/qpnyecc6rfvALIneFkxQv5YAw==","signature_status":"signed_v1","signed_at":"2026-05-18T02:35:45.610071Z","signed_message":"canonical_sha256_bytes"},"source_id":"1411.4510","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3b563a4b687fc4414330af85083ed9f28cae6a17b1e3f584530c6cce5423feb3","sha256:4f1f467e491253818ade8ef2f2918a8c89738c2877e1216b1ce5af3daf641d5d"],"state_sha256":"11c7c3f80e4bf8e6c8fb5b3adb536f277c1bee62d587e76a408df6361be61fee"}