{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:WKLVI4E7VXMAKGJNND2UW4UDCH","short_pith_number":"pith:WKLVI4E7","canonical_record":{"source":{"id":"1206.6873","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-06-27T16:30:17Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3c3c2f43ce1bf5a927173207e573b493900e5bf3191a7ad654974d481c0a34f8","abstract_canon_sha256":"db7d7ae63dc85dc72440fe7c4aeed235c07064bde37b8f8d3f1929a1253f1060"},"schema_version":"1.0"},"canonical_sha256":"b29754709fadd805192d68f54b728311f09d4eee016ea4ce9e67590873677c45","source":{"kind":"arxiv","id":"1206.6873","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1206.6873","created_at":"2026-05-18T03:52:15Z"},{"alias_kind":"arxiv_version","alias_value":"1206.6873v1","created_at":"2026-05-18T03:52:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1206.6873","created_at":"2026-05-18T03:52:15Z"},{"alias_kind":"pith_short_12","alias_value":"WKLVI4E7VXMA","created_at":"2026-05-18T12:27:25Z"},{"alias_kind":"pith_short_16","alias_value":"WKLVI4E7VXMAKGJN","created_at":"2026-05-18T12:27:25Z"},{"alias_kind":"pith_short_8","alias_value":"WKLVI4E7","created_at":"2026-05-18T12:27:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:WKLVI4E7VXMAKGJNND2UW4UDCH","target":"record","payload":{"canonical_record":{"source":{"id":"1206.6873","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-06-27T16:30:17Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3c3c2f43ce1bf5a927173207e573b493900e5bf3191a7ad654974d481c0a34f8","abstract_canon_sha256":"db7d7ae63dc85dc72440fe7c4aeed235c07064bde37b8f8d3f1929a1253f1060"},"schema_version":"1.0"},"canonical_sha256":"b29754709fadd805192d68f54b728311f09d4eee016ea4ce9e67590873677c45","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:52:15.326650Z","signature_b64":"5OUdN12daArshmn+VTQnhbbe71CqQf9xS57fk0TnORuJYModqfHFJs/gSVvX4w0vf4IbzukxAUiFYFOezOrMCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b29754709fadd805192d68f54b728311f09d4eee016ea4ce9e67590873677c45","last_reissued_at":"2026-05-18T03:52:15.326010Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:52:15.326010Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1206.6873","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":"2026-05-18T03:52:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aB3elYOj5CkAk7qXRt27V9Vyt4MsqzG27pgs5c8A16yf5/y5S8do+3uEyeHI165gNnZ/HfLKMrGKBHVd2lXjDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T03:09:36.604147Z"},"content_sha256":"873421d6aca9b44333345c33e2b31f8ef4d258ae79e3d0a846bf63bcd1ad7921","schema_version":"1.0","event_id":"sha256:873421d6aca9b44333345c33e2b31f8ef4d258ae79e3d0a846bf63bcd1ad7921"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:WKLVI4E7VXMAKGJNND2UW4UDCH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Variable noise and dimensionality reduction for sparse Gaussian processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Edward Snelson, Zoubin Ghahramani","submitted_at":"2012-06-27T16:30:17Z","abstract_excerpt":"The sparse pseudo-input Gaussian process (SPGP) is a new approximation method for speeding up GP regression in the case of a large number of data points N. The approximation is controlled by the gradient optimization of a small set of M `pseudo-inputs', thereby reducing complexity from N^3 to NM^2. One limitation of the SPGP is that this optimization space becomes impractically big for high dimensional data sets. This paper addresses this limitation by performing automatic dimensionality reduction. A projection of the input space to a low dimensional space is learned in a supervised manner, al"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1206.6873","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":"2026-05-18T03:52:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XbHwIdnz0vQ2X0F/zGQnHupjyJdIFpUQZRopGc/LVQG7VXiEioRfENj5JNo10RYU5Fk7ken/5ThQXMIJnXryBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T03:09:36.604934Z"},"content_sha256":"3d3e9ac5f975cd94eef446679b834f454918fdaf9cda79f47c3eb1282c477f65","schema_version":"1.0","event_id":"sha256:3d3e9ac5f975cd94eef446679b834f454918fdaf9cda79f47c3eb1282c477f65"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WKLVI4E7VXMAKGJNND2UW4UDCH/bundle.json","state_url":"https://pith.science/pith/WKLVI4E7VXMAKGJNND2UW4UDCH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WKLVI4E7VXMAKGJNND2UW4UDCH/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":"2026-08-22T03:09:36Z","links":{"resolver":"https://pith.science/pith/WKLVI4E7VXMAKGJNND2UW4UDCH","bundle":"https://pith.science/pith/WKLVI4E7VXMAKGJNND2UW4UDCH/bundle.json","state":"https://pith.science/pith/WKLVI4E7VXMAKGJNND2UW4UDCH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WKLVI4E7VXMAKGJNND2UW4UDCH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:WKLVI4E7VXMAKGJNND2UW4UDCH","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":"db7d7ae63dc85dc72440fe7c4aeed235c07064bde37b8f8d3f1929a1253f1060","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-06-27T16:30:17Z","title_canon_sha256":"3c3c2f43ce1bf5a927173207e573b493900e5bf3191a7ad654974d481c0a34f8"},"schema_version":"1.0","source":{"id":"1206.6873","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1206.6873","created_at":"2026-05-18T03:52:15Z"},{"alias_kind":"arxiv_version","alias_value":"1206.6873v1","created_at":"2026-05-18T03:52:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1206.6873","created_at":"2026-05-18T03:52:15Z"},{"alias_kind":"pith_short_12","alias_value":"WKLVI4E7VXMA","created_at":"2026-05-18T12:27:25Z"},{"alias_kind":"pith_short_16","alias_value":"WKLVI4E7VXMAKGJN","created_at":"2026-05-18T12:27:25Z"},{"alias_kind":"pith_short_8","alias_value":"WKLVI4E7","created_at":"2026-05-18T12:27:25Z"}],"graph_snapshots":[{"event_id":"sha256:3d3e9ac5f975cd94eef446679b834f454918fdaf9cda79f47c3eb1282c477f65","target":"graph","created_at":"2026-05-18T03:52:15Z","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":"The sparse pseudo-input Gaussian process (SPGP) is a new approximation method for speeding up GP regression in the case of a large number of data points N. The approximation is controlled by the gradient optimization of a small set of M `pseudo-inputs', thereby reducing complexity from N^3 to NM^2. One limitation of the SPGP is that this optimization space becomes impractically big for high dimensional data sets. This paper addresses this limitation by performing automatic dimensionality reduction. A projection of the input space to a low dimensional space is learned in a supervised manner, al","authors_text":"Edward Snelson, Zoubin Ghahramani","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-06-27T16:30:17Z","title":"Variable noise and dimensionality reduction for sparse Gaussian processes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1206.6873","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":"sha256:873421d6aca9b44333345c33e2b31f8ef4d258ae79e3d0a846bf63bcd1ad7921","target":"record","created_at":"2026-05-18T03:52:15Z","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":"db7d7ae63dc85dc72440fe7c4aeed235c07064bde37b8f8d3f1929a1253f1060","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-06-27T16:30:17Z","title_canon_sha256":"3c3c2f43ce1bf5a927173207e573b493900e5bf3191a7ad654974d481c0a34f8"},"schema_version":"1.0","source":{"id":"1206.6873","kind":"arxiv","version":1}},"canonical_sha256":"b29754709fadd805192d68f54b728311f09d4eee016ea4ce9e67590873677c45","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b29754709fadd805192d68f54b728311f09d4eee016ea4ce9e67590873677c45","first_computed_at":"2026-05-18T03:52:15.326010Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:52:15.326010Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5OUdN12daArshmn+VTQnhbbe71CqQf9xS57fk0TnORuJYModqfHFJs/gSVvX4w0vf4IbzukxAUiFYFOezOrMCg==","signature_status":"signed_v1","signed_at":"2026-05-18T03:52:15.326650Z","signed_message":"canonical_sha256_bytes"},"source_id":"1206.6873","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:873421d6aca9b44333345c33e2b31f8ef4d258ae79e3d0a846bf63bcd1ad7921","sha256:3d3e9ac5f975cd94eef446679b834f454918fdaf9cda79f47c3eb1282c477f65"],"state_sha256":"3849a30b69f740d725d30c56cd3fde731fc12a2bf60665ceef52d6a9613ea983"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xYqJXIy9Oz6SG5zsEF97Mv6IK/n+2/IJVLoHX0gHqJojpkXPo8cqI+s2u3nk5Qh0pT9CTN4knc0idnZeGI8xAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T03:09:36.612585Z","bundle_sha256":"c98257ee6da89f32f54e6a90ad536601ac0d5f568b0fd41b89eb34e84d60ad2a"}}