{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:U5YS7LNVNTA5VQAFTOVGGSDSDU","short_pith_number":"pith:U5YS7LNV","canonical_record":{"source":{"id":"2507.06839","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-09T13:39:37Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"06fdd3b947e4b158e03b7c0aa910daa0bf49b13b7f88d27e81ebf2e272f1d909","abstract_canon_sha256":"bdcee9df59b8da44b69e66b59a92c935eae9eea167948c5e9bb3f5b8af31de43"},"schema_version":"1.0"},"canonical_sha256":"a7712fadb56cc1dac0059baa6348721d19fa4dabc3ecfa70791c1524f7870259","source":{"kind":"arxiv","id":"2507.06839","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.06839","created_at":"2026-07-05T11:34:23Z"},{"alias_kind":"arxiv_version","alias_value":"2507.06839v1","created_at":"2026-07-05T11:34:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.06839","created_at":"2026-07-05T11:34:23Z"},{"alias_kind":"pith_short_12","alias_value":"U5YS7LNVNTA5","created_at":"2026-07-05T11:34:23Z"},{"alias_kind":"pith_short_16","alias_value":"U5YS7LNVNTA5VQAF","created_at":"2026-07-05T11:34:23Z"},{"alias_kind":"pith_short_8","alias_value":"U5YS7LNV","created_at":"2026-07-05T11:34:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:U5YS7LNVNTA5VQAFTOVGGSDSDU","target":"record","payload":{"canonical_record":{"source":{"id":"2507.06839","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-09T13:39:37Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"06fdd3b947e4b158e03b7c0aa910daa0bf49b13b7f88d27e81ebf2e272f1d909","abstract_canon_sha256":"bdcee9df59b8da44b69e66b59a92c935eae9eea167948c5e9bb3f5b8af31de43"},"schema_version":"1.0"},"canonical_sha256":"a7712fadb56cc1dac0059baa6348721d19fa4dabc3ecfa70791c1524f7870259","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:34:23.938568Z","signature_b64":"Q9d+ZlfkSac1NR7fp7b5b0bZ16fZCiPvH1vht0/XJC1vY9xb0iCJ5kWHKXDCUldHWYuffETdQw6tEj7UfatbDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a7712fadb56cc1dac0059baa6348721d19fa4dabc3ecfa70791c1524f7870259","last_reissued_at":"2026-07-05T11:34:23.938103Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:34:23.938103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.06839","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":"2026-07-05T11:34:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jfjsfKlS12eD9+5wGncHdG/CZcfSjLuzo1b+n2wrTRYcEBMaCbB2VH89igY8hgWpDWoVrKpmzYP4i/+l/Sf4CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T13:50:52.420829Z"},"content_sha256":"a6e9e65ec322caf2a9a16f9ec271708d4b5b85aae106a5fea2528d4289ce4c76","schema_version":"1.0","event_id":"sha256:a6e9e65ec322caf2a9a16f9ec271708d4b5b85aae106a5fea2528d4289ce4c76"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:U5YS7LNVNTA5VQAFTOVGGSDSDU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Scalable Gaussian Processes: Advances in Iterative Methods and Pathwise Conditioning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jihao Andreas Lin","submitted_at":"2025-07-09T13:39:37Z","abstract_excerpt":"Gaussian processes are a powerful framework for uncertainty-aware function approximation and sequential decision-making. Unfortunately, their classical formulation does not scale gracefully to large amounts of data and modern hardware for massively-parallel computation, prompting many researchers to develop techniques which improve their scalability. This dissertation focuses on the powerful combination of iterative methods and pathwise conditioning to develop methodological contributions which facilitate the use of Gaussian processes in modern large-scale settings. By combining these two tech"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.06839","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2507.06839/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":"2026-07-05T11:34:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iGgAVqPUR2x37V+H9FZjMEQfA18NhPR6tm9pv/gyDsDVYNGDkUqQy02VX2BJX5gTdSegeni3rkm4IwhluKb8Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T13:50:52.421345Z"},"content_sha256":"16cb29cc761ea8a9e64007d31bbff35cea0cf59fda88810e7d6b752e1e4d9b37","schema_version":"1.0","event_id":"sha256:16cb29cc761ea8a9e64007d31bbff35cea0cf59fda88810e7d6b752e1e4d9b37"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U5YS7LNVNTA5VQAFTOVGGSDSDU/bundle.json","state_url":"https://pith.science/pith/U5YS7LNVNTA5VQAFTOVGGSDSDU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U5YS7LNVNTA5VQAFTOVGGSDSDU/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":"2026-08-14T13:50:52Z","links":{"resolver":"https://pith.science/pith/U5YS7LNVNTA5VQAFTOVGGSDSDU","bundle":"https://pith.science/pith/U5YS7LNVNTA5VQAFTOVGGSDSDU/bundle.json","state":"https://pith.science/pith/U5YS7LNVNTA5VQAFTOVGGSDSDU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U5YS7LNVNTA5VQAFTOVGGSDSDU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:U5YS7LNVNTA5VQAFTOVGGSDSDU","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":"bdcee9df59b8da44b69e66b59a92c935eae9eea167948c5e9bb3f5b8af31de43","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-09T13:39:37Z","title_canon_sha256":"06fdd3b947e4b158e03b7c0aa910daa0bf49b13b7f88d27e81ebf2e272f1d909"},"schema_version":"1.0","source":{"id":"2507.06839","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.06839","created_at":"2026-07-05T11:34:23Z"},{"alias_kind":"arxiv_version","alias_value":"2507.06839v1","created_at":"2026-07-05T11:34:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.06839","created_at":"2026-07-05T11:34:23Z"},{"alias_kind":"pith_short_12","alias_value":"U5YS7LNVNTA5","created_at":"2026-07-05T11:34:23Z"},{"alias_kind":"pith_short_16","alias_value":"U5YS7LNVNTA5VQAF","created_at":"2026-07-05T11:34:23Z"},{"alias_kind":"pith_short_8","alias_value":"U5YS7LNV","created_at":"2026-07-05T11:34:23Z"}],"graph_snapshots":[{"event_id":"sha256:16cb29cc761ea8a9e64007d31bbff35cea0cf59fda88810e7d6b752e1e4d9b37","target":"graph","created_at":"2026-07-05T11:34:23Z","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/2507.06839/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Gaussian processes are a powerful framework for uncertainty-aware function approximation and sequential decision-making. Unfortunately, their classical formulation does not scale gracefully to large amounts of data and modern hardware for massively-parallel computation, prompting many researchers to develop techniques which improve their scalability. This dissertation focuses on the powerful combination of iterative methods and pathwise conditioning to develop methodological contributions which facilitate the use of Gaussian processes in modern large-scale settings. By combining these two tech","authors_text":"Jihao Andreas Lin","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-09T13:39:37Z","title":"Scalable Gaussian Processes: Advances in Iterative Methods and Pathwise Conditioning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.06839","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:a6e9e65ec322caf2a9a16f9ec271708d4b5b85aae106a5fea2528d4289ce4c76","target":"record","created_at":"2026-07-05T11:34:23Z","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":"bdcee9df59b8da44b69e66b59a92c935eae9eea167948c5e9bb3f5b8af31de43","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-09T13:39:37Z","title_canon_sha256":"06fdd3b947e4b158e03b7c0aa910daa0bf49b13b7f88d27e81ebf2e272f1d909"},"schema_version":"1.0","source":{"id":"2507.06839","kind":"arxiv","version":1}},"canonical_sha256":"a7712fadb56cc1dac0059baa6348721d19fa4dabc3ecfa70791c1524f7870259","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a7712fadb56cc1dac0059baa6348721d19fa4dabc3ecfa70791c1524f7870259","first_computed_at":"2026-07-05T11:34:23.938103Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:34:23.938103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Q9d+ZlfkSac1NR7fp7b5b0bZ16fZCiPvH1vht0/XJC1vY9xb0iCJ5kWHKXDCUldHWYuffETdQw6tEj7UfatbDw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:34:23.938568Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.06839","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a6e9e65ec322caf2a9a16f9ec271708d4b5b85aae106a5fea2528d4289ce4c76","sha256:16cb29cc761ea8a9e64007d31bbff35cea0cf59fda88810e7d6b752e1e4d9b37"],"state_sha256":"7756cb5e8e87cfcc268ee3c6640e62fd04793e4b6de6bbabecf9a14a4848481d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Xow6sBMcO41VwMCnnI2M/aK4vyZmB5xP98l3jpbWtuyprXTv7ENPVPUvqsYRO6AA9Sw2Hp4xhKMQ1rWnakMcBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T13:50:52.427400Z","bundle_sha256":"4fd22ecedc6f2646778b92e05701f614cbcc8a0ff08eb1a4ebff6bf4192dd69f"}}