{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:SI6B3CIROO2YOJFDQU2YM7LWNG","short_pith_number":"pith:SI6B3CIR","canonical_record":{"source":{"id":"2309.10068","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-09-18T18:34:51Z","cross_cats_sorted":["cs.LG","math.PR"],"title_canon_sha256":"00accfc0899044006db3c8400542748628f4ee26dd69ce147fb8ddfc778282b9","abstract_canon_sha256":"e752b947fd0010dee7ddefeda43f4f9af64fcfcc302fcf116b8db7ad6c4c3805"},"schema_version":"1.0"},"canonical_sha256":"923c1d891173b58724a38535867d766995984871eba1a897342b2d87754bac9f","source":{"kind":"arxiv","id":"2309.10068","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.10068","created_at":"2026-07-05T09:13:47Z"},{"alias_kind":"arxiv_version","alias_value":"2309.10068v2","created_at":"2026-07-05T09:13:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.10068","created_at":"2026-07-05T09:13:47Z"},{"alias_kind":"pith_short_12","alias_value":"SI6B3CIROO2Y","created_at":"2026-07-05T09:13:47Z"},{"alias_kind":"pith_short_16","alias_value":"SI6B3CIROO2YOJFD","created_at":"2026-07-05T09:13:47Z"},{"alias_kind":"pith_short_8","alias_value":"SI6B3CIR","created_at":"2026-07-05T09:13:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:SI6B3CIROO2YOJFDQU2YM7LWNG","target":"record","payload":{"canonical_record":{"source":{"id":"2309.10068","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-09-18T18:34:51Z","cross_cats_sorted":["cs.LG","math.PR"],"title_canon_sha256":"00accfc0899044006db3c8400542748628f4ee26dd69ce147fb8ddfc778282b9","abstract_canon_sha256":"e752b947fd0010dee7ddefeda43f4f9af64fcfcc302fcf116b8db7ad6c4c3805"},"schema_version":"1.0"},"canonical_sha256":"923c1d891173b58724a38535867d766995984871eba1a897342b2d87754bac9f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:13:47.716367Z","signature_b64":"+BsfZ+vWAW+stUxXraQOAcbamC2amtpfQwqzvD0bMorW667qv3Orxsnnc/xIuDqBu8vGCU7gdj6LLGLJwHe0Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"923c1d891173b58724a38535867d766995984871eba1a897342b2d87754bac9f","last_reissued_at":"2026-07-05T09:13:47.715852Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:13:47.715852Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.10068","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:13:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nXYLkGDYepUqPNdbfWusNrQMfONSyk4Yd/MlaSPjWAW3KYraPijG5zk5gywTTKFzR3//gHHQQ3CW2EQWq63UBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T16:17:00.148459Z"},"content_sha256":"8bf7cf361f83c053dff9868194eca9ded0eafc69423a11a9b93c75a1c2c5cde0","schema_version":"1.0","event_id":"sha256:8bf7cf361f83c053dff9868194eca9ded0eafc69423a11a9b93c75a1c2c5cde0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:SI6B3CIROO2YOJFDQU2YM7LWNG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","math.PR"],"primary_cat":"stat.ML","authors_text":"Hengrui Luo, Marcus M. Noack, Mark D. Risser","submitted_at":"2023-09-18T18:34:51Z","abstract_excerpt":"The Gaussian process (GP) is a popular statistical technique for stochastic function approximation and uncertainty quantification from data. GPs have been adopted into the realm of machine learning in the last two decades because of their superior prediction abilities, especially in data-sparse scenarios, and their inherent ability to provide robust uncertainty estimates. Even so, their performance highly depends on intricate customizations of the core methodology, which often leads to dissatisfaction among practitioners when standard setups and off-the-shelf software tools are being deployed."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.10068","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2309.10068/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T09:13:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vbWAnG4fdx1buSYBZaaGBjjzbQeZeXFLuyi33FiKb2UYtMzMAxGDdvFfvAm2h/WorwFy8xJMO/lLBzCEsu6bDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T16:17:00.149269Z"},"content_sha256":"57e16e96a9d85ba9c30d51f8e09b9c187474167115646a1e69112856f1c4a7ec","schema_version":"1.0","event_id":"sha256:57e16e96a9d85ba9c30d51f8e09b9c187474167115646a1e69112856f1c4a7ec"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SI6B3CIROO2YOJFDQU2YM7LWNG/bundle.json","state_url":"https://pith.science/pith/SI6B3CIROO2YOJFDQU2YM7LWNG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SI6B3CIROO2YOJFDQU2YM7LWNG/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":"2026-08-03T16:17:00Z","links":{"resolver":"https://pith.science/pith/SI6B3CIROO2YOJFDQU2YM7LWNG","bundle":"https://pith.science/pith/SI6B3CIROO2YOJFDQU2YM7LWNG/bundle.json","state":"https://pith.science/pith/SI6B3CIROO2YOJFDQU2YM7LWNG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SI6B3CIROO2YOJFDQU2YM7LWNG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:SI6B3CIROO2YOJFDQU2YM7LWNG","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":"e752b947fd0010dee7ddefeda43f4f9af64fcfcc302fcf116b8db7ad6c4c3805","cross_cats_sorted":["cs.LG","math.PR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-09-18T18:34:51Z","title_canon_sha256":"00accfc0899044006db3c8400542748628f4ee26dd69ce147fb8ddfc778282b9"},"schema_version":"1.0","source":{"id":"2309.10068","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.10068","created_at":"2026-07-05T09:13:47Z"},{"alias_kind":"arxiv_version","alias_value":"2309.10068v2","created_at":"2026-07-05T09:13:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.10068","created_at":"2026-07-05T09:13:47Z"},{"alias_kind":"pith_short_12","alias_value":"SI6B3CIROO2Y","created_at":"2026-07-05T09:13:47Z"},{"alias_kind":"pith_short_16","alias_value":"SI6B3CIROO2YOJFD","created_at":"2026-07-05T09:13:47Z"},{"alias_kind":"pith_short_8","alias_value":"SI6B3CIR","created_at":"2026-07-05T09:13:47Z"}],"graph_snapshots":[{"event_id":"sha256:57e16e96a9d85ba9c30d51f8e09b9c187474167115646a1e69112856f1c4a7ec","target":"graph","created_at":"2026-07-05T09:13:47Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2309.10068/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The Gaussian process (GP) is a popular statistical technique for stochastic function approximation and uncertainty quantification from data. GPs have been adopted into the realm of machine learning in the last two decades because of their superior prediction abilities, especially in data-sparse scenarios, and their inherent ability to provide robust uncertainty estimates. Even so, their performance highly depends on intricate customizations of the core methodology, which often leads to dissatisfaction among practitioners when standard setups and off-the-shelf software tools are being deployed.","authors_text":"Hengrui Luo, Marcus M. Noack, Mark D. Risser","cross_cats":["cs.LG","math.PR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-09-18T18:34:51Z","title":"A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.10068","kind":"arxiv","version":2},"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":"sha256:8bf7cf361f83c053dff9868194eca9ded0eafc69423a11a9b93c75a1c2c5cde0","target":"record","created_at":"2026-07-05T09:13:47Z","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":"e752b947fd0010dee7ddefeda43f4f9af64fcfcc302fcf116b8db7ad6c4c3805","cross_cats_sorted":["cs.LG","math.PR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-09-18T18:34:51Z","title_canon_sha256":"00accfc0899044006db3c8400542748628f4ee26dd69ce147fb8ddfc778282b9"},"schema_version":"1.0","source":{"id":"2309.10068","kind":"arxiv","version":2}},"canonical_sha256":"923c1d891173b58724a38535867d766995984871eba1a897342b2d87754bac9f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"923c1d891173b58724a38535867d766995984871eba1a897342b2d87754bac9f","first_computed_at":"2026-07-05T09:13:47.715852Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:13:47.715852Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+BsfZ+vWAW+stUxXraQOAcbamC2amtpfQwqzvD0bMorW667qv3Orxsnnc/xIuDqBu8vGCU7gdj6LLGLJwHe0Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:13:47.716367Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.10068","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8bf7cf361f83c053dff9868194eca9ded0eafc69423a11a9b93c75a1c2c5cde0","sha256:57e16e96a9d85ba9c30d51f8e09b9c187474167115646a1e69112856f1c4a7ec"],"state_sha256":"2c5c5d7f5bcffd5d20964556da258b149add031d8ca2eb16207f4647daf2677d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9oB3VY//BWNV7JyBGP4SLJTggGjpOwKjRbRQVpIuUcfG5ebW0UjK/ZQBTYWFeqH7Ywb0vaEAAYBPlmv+3C37AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T16:17:00.157306Z","bundle_sha256":"6db960b66c9c7109190d62d8e6500bd9145e542593dfc0db34bc5872a4a1aed9"}}