{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:EFZ5N4CYTFNEDR7K6BK4YHHPTX","short_pith_number":"pith:EFZ5N4CY","canonical_record":{"source":{"id":"1402.1389","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-06T16:08:40Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"818fcfdcd0e69c8993d704162594a84904df9768de422220a444a1575b083b0d","abstract_canon_sha256":"cc73689ea33bed09193edaa0a6df1a986e54e725daa5584968bd309a52a22cbe"},"schema_version":"1.0"},"canonical_sha256":"2173d6f058995a41c7eaf055cc1cef9dde93a2477d88c02feb79d42a620d36de","source":{"kind":"arxiv","id":"1402.1389","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1402.1389","created_at":"2026-05-18T02:41:26Z"},{"alias_kind":"arxiv_version","alias_value":"1402.1389v2","created_at":"2026-05-18T02:41:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1402.1389","created_at":"2026-05-18T02:41:26Z"},{"alias_kind":"pith_short_12","alias_value":"EFZ5N4CYTFNE","created_at":"2026-05-18T12:28:25Z"},{"alias_kind":"pith_short_16","alias_value":"EFZ5N4CYTFNEDR7K","created_at":"2026-05-18T12:28:25Z"},{"alias_kind":"pith_short_8","alias_value":"EFZ5N4CY","created_at":"2026-05-18T12:28:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:EFZ5N4CYTFNEDR7K6BK4YHHPTX","target":"record","payload":{"canonical_record":{"source":{"id":"1402.1389","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-06T16:08:40Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"818fcfdcd0e69c8993d704162594a84904df9768de422220a444a1575b083b0d","abstract_canon_sha256":"cc73689ea33bed09193edaa0a6df1a986e54e725daa5584968bd309a52a22cbe"},"schema_version":"1.0"},"canonical_sha256":"2173d6f058995a41c7eaf055cc1cef9dde93a2477d88c02feb79d42a620d36de","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:41:26.114054Z","signature_b64":"b0vdgMtAZ3qdLsTjQHdFZSSoYaAbpVz6lOEZA/cvq0pToiF1gsEdw3fFiy/buJRQcbHeAsY89XDJIWSCZ9uoCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2173d6f058995a41c7eaf055cc1cef9dde93a2477d88c02feb79d42a620d36de","last_reissued_at":"2026-05-18T02:41:26.113619Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:41:26.113619Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1402.1389","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":"2026-05-18T02:41:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9LFVpvaGjhk8BgkJbVh4jykoqP4yL8lYyFnV5s7ZLkgxZ28et8tIBDiN3nCcbiXBCMzpV9CFvug8Vi9JTWb6DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T14:03:50.178072Z"},"content_sha256":"6c4b2d6a88dfa204fe0a35f32decf4be3276b483abe376b77f77adf3535c3eaa","schema_version":"1.0","event_id":"sha256:6c4b2d6a88dfa204fe0a35f32decf4be3276b483abe376b77f77adf3535c3eaa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:EFZ5N4CYTFNEDR7K6BK4YHHPTX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Carl E. Rasmussen, Mark van der Wilk, Yarin Gal","submitted_at":"2014-02-06T16:08:40Z","abstract_excerpt":"Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions. They have been applied to both regression and non-linear dimensionality reduction, and offer desirable properties such as uncertainty estimates, robustness to over-fitting, and principled ways for tuning hyper-parameters. However the scalability of these models to big datasets remains an active topic of research. We introduce a novel re-parametrisation of variational inference for sparse GP regression and latent variable models that allows for an efficient distributed algorithm. This is done by exploiting "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.1389","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":""},"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":"2026-05-18T02:41:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AK1x+F1KWKwnW4dX7U0Y/mKCDZ8FUM8+Q8bg3cQYkzF6tuS/wqC5cEAgU/Ybsbzj2jK6u1OrReemLhvvwkB7Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T14:03:50.178428Z"},"content_sha256":"c624b3fda7533535487a68774980defd2ddf227a132494ffc8c78569a939cd90","schema_version":"1.0","event_id":"sha256:c624b3fda7533535487a68774980defd2ddf227a132494ffc8c78569a939cd90"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EFZ5N4CYTFNEDR7K6BK4YHHPTX/bundle.json","state_url":"https://pith.science/pith/EFZ5N4CYTFNEDR7K6BK4YHHPTX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EFZ5N4CYTFNEDR7K6BK4YHHPTX/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":"2026-06-28T14:03:50Z","links":{"resolver":"https://pith.science/pith/EFZ5N4CYTFNEDR7K6BK4YHHPTX","bundle":"https://pith.science/pith/EFZ5N4CYTFNEDR7K6BK4YHHPTX/bundle.json","state":"https://pith.science/pith/EFZ5N4CYTFNEDR7K6BK4YHHPTX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EFZ5N4CYTFNEDR7K6BK4YHHPTX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:EFZ5N4CYTFNEDR7K6BK4YHHPTX","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":"cc73689ea33bed09193edaa0a6df1a986e54e725daa5584968bd309a52a22cbe","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-06T16:08:40Z","title_canon_sha256":"818fcfdcd0e69c8993d704162594a84904df9768de422220a444a1575b083b0d"},"schema_version":"1.0","source":{"id":"1402.1389","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1402.1389","created_at":"2026-05-18T02:41:26Z"},{"alias_kind":"arxiv_version","alias_value":"1402.1389v2","created_at":"2026-05-18T02:41:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1402.1389","created_at":"2026-05-18T02:41:26Z"},{"alias_kind":"pith_short_12","alias_value":"EFZ5N4CYTFNE","created_at":"2026-05-18T12:28:25Z"},{"alias_kind":"pith_short_16","alias_value":"EFZ5N4CYTFNEDR7K","created_at":"2026-05-18T12:28:25Z"},{"alias_kind":"pith_short_8","alias_value":"EFZ5N4CY","created_at":"2026-05-18T12:28:25Z"}],"graph_snapshots":[{"event_id":"sha256:c624b3fda7533535487a68774980defd2ddf227a132494ffc8c78569a939cd90","target":"graph","created_at":"2026-05-18T02:41:26Z","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) are a powerful tool for probabilistic inference over functions. They have been applied to both regression and non-linear dimensionality reduction, and offer desirable properties such as uncertainty estimates, robustness to over-fitting, and principled ways for tuning hyper-parameters. However the scalability of these models to big datasets remains an active topic of research. We introduce a novel re-parametrisation of variational inference for sparse GP regression and latent variable models that allows for an efficient distributed algorithm. This is done by exploiting ","authors_text":"Carl E. Rasmussen, Mark van der Wilk, Yarin Gal","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-06T16:08:40Z","title":"Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.1389","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":"sha256:6c4b2d6a88dfa204fe0a35f32decf4be3276b483abe376b77f77adf3535c3eaa","target":"record","created_at":"2026-05-18T02:41:26Z","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":"cc73689ea33bed09193edaa0a6df1a986e54e725daa5584968bd309a52a22cbe","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-06T16:08:40Z","title_canon_sha256":"818fcfdcd0e69c8993d704162594a84904df9768de422220a444a1575b083b0d"},"schema_version":"1.0","source":{"id":"1402.1389","kind":"arxiv","version":2}},"canonical_sha256":"2173d6f058995a41c7eaf055cc1cef9dde93a2477d88c02feb79d42a620d36de","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2173d6f058995a41c7eaf055cc1cef9dde93a2477d88c02feb79d42a620d36de","first_computed_at":"2026-05-18T02:41:26.113619Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:41:26.113619Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"b0vdgMtAZ3qdLsTjQHdFZSSoYaAbpVz6lOEZA/cvq0pToiF1gsEdw3fFiy/buJRQcbHeAsY89XDJIWSCZ9uoCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:41:26.114054Z","signed_message":"canonical_sha256_bytes"},"source_id":"1402.1389","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6c4b2d6a88dfa204fe0a35f32decf4be3276b483abe376b77f77adf3535c3eaa","sha256:c624b3fda7533535487a68774980defd2ddf227a132494ffc8c78569a939cd90"],"state_sha256":"b4c290a57307e6a4043cc584d00896c1594d89ee2fac0be4bd75c65f851e5e02"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zA3v/4bcZiY8WzhOOeHsZx5emJSdRujrooQzx0SqHlcsiodt3lR2U7/9p5S4Zbf4b5FFg2+rvgxiyIP3a4SoDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-28T14:03:50.180259Z","bundle_sha256":"010a950b48bfee93c3a8df1507478c3e63e1b41738a8e3277ccb16233ccc6a8e"}}