{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:EA5XTW5KPODDDNVU7FCZVESTUJ","short_pith_number":"pith:EA5XTW5K","canonical_record":{"source":{"id":"1705.10813","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-30T18:19:16Z","cross_cats_sorted":[],"title_canon_sha256":"d52d6bab9d1de5d21329df75e337bcbc58d9a5a358b2f741c62b7b063fd231dd","abstract_canon_sha256":"5164f492c692f422c3378795811ab849b58596a1562a5f0b87ffb7a0f742aca6"},"schema_version":"1.0"},"canonical_sha256":"203b79dbaa7b8631b6b4f9459a9253a27ab3ff73e0a2a5508dd637f70c6b7301","source":{"kind":"arxiv","id":"1705.10813","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.10813","created_at":"2026-05-18T00:32:20Z"},{"alias_kind":"arxiv_version","alias_value":"1705.10813v3","created_at":"2026-05-18T00:32:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.10813","created_at":"2026-05-18T00:32:20Z"},{"alias_kind":"pith_short_12","alias_value":"EA5XTW5KPODD","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"EA5XTW5KPODDDNVU","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"EA5XTW5K","created_at":"2026-05-18T12:31:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:EA5XTW5KPODDDNVU7FCZVESTUJ","target":"record","payload":{"canonical_record":{"source":{"id":"1705.10813","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-30T18:19:16Z","cross_cats_sorted":[],"title_canon_sha256":"d52d6bab9d1de5d21329df75e337bcbc58d9a5a358b2f741c62b7b063fd231dd","abstract_canon_sha256":"5164f492c692f422c3378795811ab849b58596a1562a5f0b87ffb7a0f742aca6"},"schema_version":"1.0"},"canonical_sha256":"203b79dbaa7b8631b6b4f9459a9253a27ab3ff73e0a2a5508dd637f70c6b7301","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:20.483568Z","signature_b64":"6a8Og5GBQMMIb6uX0o7vhg7ItbEbjmIsAaf8etM5cjDdHMEQvVmpg3pBRr9v4xbymouWzabS8uRJ3A8eHWrmCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"203b79dbaa7b8631b6b4f9459a9253a27ab3ff73e0a2a5508dd637f70c6b7301","last_reissued_at":"2026-05-18T00:32:20.483022Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:20.483022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.10813","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:32:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/7UdPtO+DEEhUEiVHIIofQmSqCLgrPelz0WZhjuukk4QhGNd7RNOp84EWIwPnM9atLZP6WWYQJ7fbcwfyPYzDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T22:32:04.654958Z"},"content_sha256":"c432da2ceb5114769e6ec873a6bb67680c037b9267c7eda798bd04e305ce3492","schema_version":"1.0","event_id":"sha256:c432da2ceb5114769e6ec873a6bb67680c037b9267c7eda798bd04e305ce3492"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:EA5XTW5KPODDDNVU7FCZVESTUJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Large Linear Multi-output Gaussian Process Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Barbara E Engelhardt, Kai Li, Li-Fang Cheng, Vladimir Feinberg","submitted_at":"2017-05-30T18:19:16Z","abstract_excerpt":"Gaussian processes (GPs), or distributions over arbitrary functions in a continuous domain, can be generalized to the multi-output case: a linear model of coregionalization (LMC) is one approach. LMCs estimate and exploit correlations across the multiple outputs. While model estimation can be performed efficiently for single-output GPs, these assume stationarity, but in the multi-output case the cross-covariance interaction is not stationary. We propose Large Linear GP (LLGP), which circumvents the need for stationarity by inducing structure in the LMC kernel through a common grid of inputs sh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.10813","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:32:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fsbfM/9/jJgtaSREAY/9ZnnbjEWT5AdPuDLu6ZaP2vjH207tcZ2cnleB/BBhbRzhr/VluZTro/Tnbqjh+HoVAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T22:32:04.655362Z"},"content_sha256":"f1087e8a696bf09260b9cea52fa1743bf899f1db106902c9b897d3a3d60b5a4b","schema_version":"1.0","event_id":"sha256:f1087e8a696bf09260b9cea52fa1743bf899f1db106902c9b897d3a3d60b5a4b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EA5XTW5KPODDDNVU7FCZVESTUJ/bundle.json","state_url":"https://pith.science/pith/EA5XTW5KPODDDNVU7FCZVESTUJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EA5XTW5KPODDDNVU7FCZVESTUJ/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":"2026-05-30T22:32:04Z","links":{"resolver":"https://pith.science/pith/EA5XTW5KPODDDNVU7FCZVESTUJ","bundle":"https://pith.science/pith/EA5XTW5KPODDDNVU7FCZVESTUJ/bundle.json","state":"https://pith.science/pith/EA5XTW5KPODDDNVU7FCZVESTUJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EA5XTW5KPODDDNVU7FCZVESTUJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:EA5XTW5KPODDDNVU7FCZVESTUJ","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":"5164f492c692f422c3378795811ab849b58596a1562a5f0b87ffb7a0f742aca6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-30T18:19:16Z","title_canon_sha256":"d52d6bab9d1de5d21329df75e337bcbc58d9a5a358b2f741c62b7b063fd231dd"},"schema_version":"1.0","source":{"id":"1705.10813","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.10813","created_at":"2026-05-18T00:32:20Z"},{"alias_kind":"arxiv_version","alias_value":"1705.10813v3","created_at":"2026-05-18T00:32:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.10813","created_at":"2026-05-18T00:32:20Z"},{"alias_kind":"pith_short_12","alias_value":"EA5XTW5KPODD","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"EA5XTW5KPODDDNVU","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"EA5XTW5K","created_at":"2026-05-18T12:31:12Z"}],"graph_snapshots":[{"event_id":"sha256:f1087e8a696bf09260b9cea52fa1743bf899f1db106902c9b897d3a3d60b5a4b","target":"graph","created_at":"2026-05-18T00:32:20Z","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":"Gaussian processes (GPs), or distributions over arbitrary functions in a continuous domain, can be generalized to the multi-output case: a linear model of coregionalization (LMC) is one approach. LMCs estimate and exploit correlations across the multiple outputs. While model estimation can be performed efficiently for single-output GPs, these assume stationarity, but in the multi-output case the cross-covariance interaction is not stationary. We propose Large Linear GP (LLGP), which circumvents the need for stationarity by inducing structure in the LMC kernel through a common grid of inputs sh","authors_text":"Barbara E Engelhardt, Kai Li, Li-Fang Cheng, Vladimir Feinberg","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-30T18:19:16Z","title":"Large Linear Multi-output Gaussian Process Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.10813","kind":"arxiv","version":3},"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:c432da2ceb5114769e6ec873a6bb67680c037b9267c7eda798bd04e305ce3492","target":"record","created_at":"2026-05-18T00:32:20Z","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":"5164f492c692f422c3378795811ab849b58596a1562a5f0b87ffb7a0f742aca6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-30T18:19:16Z","title_canon_sha256":"d52d6bab9d1de5d21329df75e337bcbc58d9a5a358b2f741c62b7b063fd231dd"},"schema_version":"1.0","source":{"id":"1705.10813","kind":"arxiv","version":3}},"canonical_sha256":"203b79dbaa7b8631b6b4f9459a9253a27ab3ff73e0a2a5508dd637f70c6b7301","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"203b79dbaa7b8631b6b4f9459a9253a27ab3ff73e0a2a5508dd637f70c6b7301","first_computed_at":"2026-05-18T00:32:20.483022Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:32:20.483022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6a8Og5GBQMMIb6uX0o7vhg7ItbEbjmIsAaf8etM5cjDdHMEQvVmpg3pBRr9v4xbymouWzabS8uRJ3A8eHWrmCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:32:20.483568Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.10813","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c432da2ceb5114769e6ec873a6bb67680c037b9267c7eda798bd04e305ce3492","sha256:f1087e8a696bf09260b9cea52fa1743bf899f1db106902c9b897d3a3d60b5a4b"],"state_sha256":"233ccc0f0ac7c100cc64f2deca14edee33126e0b2e1b33cbce7023f36ff6f1b5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"boIhPfAGa8XG6qhFK3otNviey0lsyTpL3ElnXGbAix5xzMqHUcC7j37Jgxi2FVcI2NcUXGXtC9btE+jfGVhPDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T22:32:04.657622Z","bundle_sha256":"4a0a06c98611495f16b8c92143ea4a612f1f8526e92879372790bfc42be1e3db"}}