{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:EUIKIPDCB4LKNADB6D2CRT6UR7","short_pith_number":"pith:EUIKIPDC","canonical_record":{"source":{"id":"1711.06989","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-19T09:38:33Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a836ebe49ec09442d3a66fe345ae6e6e4beabb75555e4886df1dc419c52b41c4","abstract_canon_sha256":"333e97722276650217b739647bd97c83191ee524e41c7462859752c0fe5d852a"},"schema_version":"1.0"},"canonical_sha256":"2510a43c620f16a68061f0f428cfd48fcaf50afed16e68b42d77768d04d4ac5a","source":{"kind":"arxiv","id":"1711.06989","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.06989","created_at":"2026-05-18T00:30:14Z"},{"alias_kind":"arxiv_version","alias_value":"1711.06989v1","created_at":"2026-05-18T00:30:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.06989","created_at":"2026-05-18T00:30:14Z"},{"alias_kind":"pith_short_12","alias_value":"EUIKIPDCB4LK","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"EUIKIPDCB4LKNADB","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"EUIKIPDC","created_at":"2026-05-18T12:31:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:EUIKIPDCB4LKNADB6D2CRT6UR7","target":"record","payload":{"canonical_record":{"source":{"id":"1711.06989","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-19T09:38:33Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a836ebe49ec09442d3a66fe345ae6e6e4beabb75555e4886df1dc419c52b41c4","abstract_canon_sha256":"333e97722276650217b739647bd97c83191ee524e41c7462859752c0fe5d852a"},"schema_version":"1.0"},"canonical_sha256":"2510a43c620f16a68061f0f428cfd48fcaf50afed16e68b42d77768d04d4ac5a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:14.902118Z","signature_b64":"DybJq+Ip68fbIBdJBa7B7rZF+sOJKHsp4HC9z0iun/3KKpfeEmaOQNc8IOSzDQIXCED/0Ud1BPeYjsxqZiu8Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2510a43c620f16a68061f0f428cfd48fcaf50afed16e68b42d77768d04d4ac5a","last_reissued_at":"2026-05-18T00:30:14.901197Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:14.901197Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.06989","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:30:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3ncfEQ4FEp66Ujy2yvpDpePrFTGDwPXDAOKZaU7Y/Lrp3b8NuXnEcr3G0sBi8zRM9Cxpq8+fWdRvgGqE4NIYDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T19:26:18.862249Z"},"content_sha256":"ccb5b0c8ce9c7a73ef6e1d7dace90ea85bc52e99bad6b369c99024b1137bad61","schema_version":"1.0","event_id":"sha256:ccb5b0c8ce9c7a73ef6e1d7dace90ea85bc52e99bad6b369c99024b1137bad61"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:EUIKIPDCB4LKNADB6D2CRT6UR7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"George S. Eskander Ekladious, Shaunak D. Bopardikar","submitted_at":"2017-11-19T09:38:33Z","abstract_excerpt":"This paper presents a sequential randomized lowrank matrix factorization approach for incrementally predicting values of an unknown function at test points using the Gaussian Processes framework. It is well-known that in the Gaussian processes framework, the computational bottlenecks are the inversion of the (regularized) kernel matrix and the computation of the hyper-parameters defining the kernel. The main contributions of this paper are two-fold. First, we formalize an approach to compute the inverse of the kernel matrix using randomized matrix factorization algorithms in a streaming scenar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.06989","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:30:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YzgUB81hR6wYg7xYO2ahVPHh4IzqgMgwpaN2/36e5M6t3Js0tg6wmBEoPWsMeUdnZiuGJs9+jhJpnC6xMZ+9Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T19:26:18.862656Z"},"content_sha256":"2aadd823c3f1d1099347e669bde83edd9232662a41d4a5ad0cda8488153089d6","schema_version":"1.0","event_id":"sha256:2aadd823c3f1d1099347e669bde83edd9232662a41d4a5ad0cda8488153089d6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EUIKIPDCB4LKNADB6D2CRT6UR7/bundle.json","state_url":"https://pith.science/pith/EUIKIPDCB4LKNADB6D2CRT6UR7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EUIKIPDCB4LKNADB6D2CRT6UR7/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-03T19:26:18Z","links":{"resolver":"https://pith.science/pith/EUIKIPDCB4LKNADB6D2CRT6UR7","bundle":"https://pith.science/pith/EUIKIPDCB4LKNADB6D2CRT6UR7/bundle.json","state":"https://pith.science/pith/EUIKIPDCB4LKNADB6D2CRT6UR7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EUIKIPDCB4LKNADB6D2CRT6UR7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:EUIKIPDCB4LKNADB6D2CRT6UR7","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":"333e97722276650217b739647bd97c83191ee524e41c7462859752c0fe5d852a","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-19T09:38:33Z","title_canon_sha256":"a836ebe49ec09442d3a66fe345ae6e6e4beabb75555e4886df1dc419c52b41c4"},"schema_version":"1.0","source":{"id":"1711.06989","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.06989","created_at":"2026-05-18T00:30:14Z"},{"alias_kind":"arxiv_version","alias_value":"1711.06989v1","created_at":"2026-05-18T00:30:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.06989","created_at":"2026-05-18T00:30:14Z"},{"alias_kind":"pith_short_12","alias_value":"EUIKIPDCB4LK","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"EUIKIPDCB4LKNADB","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"EUIKIPDC","created_at":"2026-05-18T12:31:12Z"}],"graph_snapshots":[{"event_id":"sha256:2aadd823c3f1d1099347e669bde83edd9232662a41d4a5ad0cda8488153089d6","target":"graph","created_at":"2026-05-18T00:30:14Z","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":"This paper presents a sequential randomized lowrank matrix factorization approach for incrementally predicting values of an unknown function at test points using the Gaussian Processes framework. It is well-known that in the Gaussian processes framework, the computational bottlenecks are the inversion of the (regularized) kernel matrix and the computation of the hyper-parameters defining the kernel. The main contributions of this paper are two-fold. First, we formalize an approach to compute the inverse of the kernel matrix using randomized matrix factorization algorithms in a streaming scenar","authors_text":"George S. Eskander Ekladious, Shaunak D. Bopardikar","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-19T09:38:33Z","title":"Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.06989","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:ccb5b0c8ce9c7a73ef6e1d7dace90ea85bc52e99bad6b369c99024b1137bad61","target":"record","created_at":"2026-05-18T00:30:14Z","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":"333e97722276650217b739647bd97c83191ee524e41c7462859752c0fe5d852a","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-19T09:38:33Z","title_canon_sha256":"a836ebe49ec09442d3a66fe345ae6e6e4beabb75555e4886df1dc419c52b41c4"},"schema_version":"1.0","source":{"id":"1711.06989","kind":"arxiv","version":1}},"canonical_sha256":"2510a43c620f16a68061f0f428cfd48fcaf50afed16e68b42d77768d04d4ac5a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2510a43c620f16a68061f0f428cfd48fcaf50afed16e68b42d77768d04d4ac5a","first_computed_at":"2026-05-18T00:30:14.901197Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:30:14.901197Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DybJq+Ip68fbIBdJBa7B7rZF+sOJKHsp4HC9z0iun/3KKpfeEmaOQNc8IOSzDQIXCED/0Ud1BPeYjsxqZiu8Aw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:30:14.902118Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.06989","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ccb5b0c8ce9c7a73ef6e1d7dace90ea85bc52e99bad6b369c99024b1137bad61","sha256:2aadd823c3f1d1099347e669bde83edd9232662a41d4a5ad0cda8488153089d6"],"state_sha256":"0139e5c16b56ef073354b8fa561ebde4fe009ce7f11efdf1f9e05d3705459e22"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sp/4MDHPkuXNgxWpITAzltgskgEizjMgwwvGoS9uOU5TvICssW2CUxk5bGFCAqApmIYo1IZ0gN/vxrhOLiz1CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-03T19:26:18.864501Z","bundle_sha256":"c069ec55d679ad1fbc9137a5840c24f4865960ebe8df22ec9d57630628c9e4cc"}}