{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:LEGPGCJJFR3R6ZRZGP7XTSORB7","short_pith_number":"pith:LEGPGCJJ","canonical_record":{"source":{"id":"1807.02537","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-07-06T18:18:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"50bed68fd4009cafa2bbfe7eadfa7f942b3c43cfbb18cf35ef5f1f91c44812e5","abstract_canon_sha256":"56291aef63646296d4399e995bc3fe317c01617c1ec28396af2daa4919c9d27b"},"schema_version":"1.0"},"canonical_sha256":"590cf309292c771f663933ff79c9d10fde992d3b44fe2652ddb73bff936a88fa","source":{"kind":"arxiv","id":"1807.02537","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.02537","created_at":"2026-05-18T00:10:51Z"},{"alias_kind":"arxiv_version","alias_value":"1807.02537v2","created_at":"2026-05-18T00:10:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.02537","created_at":"2026-05-18T00:10:51Z"},{"alias_kind":"pith_short_12","alias_value":"LEGPGCJJFR3R","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LEGPGCJJFR3R6ZRZ","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LEGPGCJJ","created_at":"2026-05-18T12:32:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:LEGPGCJJFR3R6ZRZGP7XTSORB7","target":"record","payload":{"canonical_record":{"source":{"id":"1807.02537","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-07-06T18:18:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"50bed68fd4009cafa2bbfe7eadfa7f942b3c43cfbb18cf35ef5f1f91c44812e5","abstract_canon_sha256":"56291aef63646296d4399e995bc3fe317c01617c1ec28396af2daa4919c9d27b"},"schema_version":"1.0"},"canonical_sha256":"590cf309292c771f663933ff79c9d10fde992d3b44fe2652ddb73bff936a88fa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:51.567812Z","signature_b64":"jymvQobYySTGJls7Y6Xg6QB9AN+Hfjb8iMkgTc5+DLWs6mMDHKSo9TrSRmtUMJ3IXWLxzKEGNtt8Ke5UVCNXDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"590cf309292c771f663933ff79c9d10fde992d3b44fe2652ddb73bff936a88fa","last_reissued_at":"2026-05-18T00:10:51.567357Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:51.567357Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.02537","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":"2026-05-18T00:10:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rcuzaShjUycRGcv9hZSvqok9ArxsbnhX01B9L6b8K3XgOmeqmvu99JXLsYrcfntOv/lLyCEo5pOj3aMwQ+hQDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T02:50:14.590287Z"},"content_sha256":"b9c885ca76c2ee5b710add6cad422e92689d6990db1253f9cea1d4ba82443735","schema_version":"1.0","event_id":"sha256:b9c885ca76c2ee5b710add6cad422e92689d6990db1253f9cea1d4ba82443735"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:LEGPGCJJFR3R6ZRZGP7XTSORB7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fully Scalable Gaussian Processes using Subspace Inducing Inputs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Aristeidis Panos, Michalis K. Titsias, Petros Dellaportas","submitted_at":"2018-07-06T18:18:36Z","abstract_excerpt":"We introduce fully scalable Gaussian processes, an implementation scheme that tackles the problem of treating a high number of training instances together with high dimensional input data. Our key idea is a representation trick over the inducing variables called subspace inducing inputs. This is combined with certain matrix-preconditioning based parametrizations of the variational distributions that lead to simplified and numerically stable variational lower bounds. Our illustrative applications are based on challenging extreme multi-label classification problems with the extra burden of the v"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.02537","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":"2026-05-18T00:10:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kjt6Zvdh5JagGAWy5FLLQPYq8uAr9UrkfNVbepw+cy7u1+0jZwKOmRTM2SkPSCD2eNld9byeYC51fx8wJNMLDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T02:50:14.590656Z"},"content_sha256":"4711f610d2cac2f7502d0d668ccf711c6f57e34f411f02d63b42cd1074fabf4b","schema_version":"1.0","event_id":"sha256:4711f610d2cac2f7502d0d668ccf711c6f57e34f411f02d63b42cd1074fabf4b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LEGPGCJJFR3R6ZRZGP7XTSORB7/bundle.json","state_url":"https://pith.science/pith/LEGPGCJJFR3R6ZRZGP7XTSORB7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LEGPGCJJFR3R6ZRZGP7XTSORB7/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":"2026-06-03T02:50:14Z","links":{"resolver":"https://pith.science/pith/LEGPGCJJFR3R6ZRZGP7XTSORB7","bundle":"https://pith.science/pith/LEGPGCJJFR3R6ZRZGP7XTSORB7/bundle.json","state":"https://pith.science/pith/LEGPGCJJFR3R6ZRZGP7XTSORB7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LEGPGCJJFR3R6ZRZGP7XTSORB7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:LEGPGCJJFR3R6ZRZGP7XTSORB7","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":"56291aef63646296d4399e995bc3fe317c01617c1ec28396af2daa4919c9d27b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-07-06T18:18:36Z","title_canon_sha256":"50bed68fd4009cafa2bbfe7eadfa7f942b3c43cfbb18cf35ef5f1f91c44812e5"},"schema_version":"1.0","source":{"id":"1807.02537","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.02537","created_at":"2026-05-18T00:10:51Z"},{"alias_kind":"arxiv_version","alias_value":"1807.02537v2","created_at":"2026-05-18T00:10:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.02537","created_at":"2026-05-18T00:10:51Z"},{"alias_kind":"pith_short_12","alias_value":"LEGPGCJJFR3R","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LEGPGCJJFR3R6ZRZ","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LEGPGCJJ","created_at":"2026-05-18T12:32:37Z"}],"graph_snapshots":[{"event_id":"sha256:4711f610d2cac2f7502d0d668ccf711c6f57e34f411f02d63b42cd1074fabf4b","target":"graph","created_at":"2026-05-18T00:10:51Z","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":"We introduce fully scalable Gaussian processes, an implementation scheme that tackles the problem of treating a high number of training instances together with high dimensional input data. Our key idea is a representation trick over the inducing variables called subspace inducing inputs. This is combined with certain matrix-preconditioning based parametrizations of the variational distributions that lead to simplified and numerically stable variational lower bounds. Our illustrative applications are based on challenging extreme multi-label classification problems with the extra burden of the v","authors_text":"Aristeidis Panos, Michalis K. Titsias, Petros Dellaportas","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-07-06T18:18:36Z","title":"Fully Scalable Gaussian Processes using Subspace Inducing Inputs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.02537","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:b9c885ca76c2ee5b710add6cad422e92689d6990db1253f9cea1d4ba82443735","target":"record","created_at":"2026-05-18T00:10:51Z","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":"56291aef63646296d4399e995bc3fe317c01617c1ec28396af2daa4919c9d27b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-07-06T18:18:36Z","title_canon_sha256":"50bed68fd4009cafa2bbfe7eadfa7f942b3c43cfbb18cf35ef5f1f91c44812e5"},"schema_version":"1.0","source":{"id":"1807.02537","kind":"arxiv","version":2}},"canonical_sha256":"590cf309292c771f663933ff79c9d10fde992d3b44fe2652ddb73bff936a88fa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"590cf309292c771f663933ff79c9d10fde992d3b44fe2652ddb73bff936a88fa","first_computed_at":"2026-05-18T00:10:51.567357Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:10:51.567357Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jymvQobYySTGJls7Y6Xg6QB9AN+Hfjb8iMkgTc5+DLWs6mMDHKSo9TrSRmtUMJ3IXWLxzKEGNtt8Ke5UVCNXDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:10:51.567812Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.02537","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b9c885ca76c2ee5b710add6cad422e92689d6990db1253f9cea1d4ba82443735","sha256:4711f610d2cac2f7502d0d668ccf711c6f57e34f411f02d63b42cd1074fabf4b"],"state_sha256":"fb8a6bc0356b768787a788189826559fc298a53281451643ad7ae93ed0cacb6f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H48BgVXtAwRDL4yVf4W5CSlMWsSMyfd5OB3hHsfxow/c89CIPfQxaERLA+myV1MOg4C6vI61hvHk4L9CGV2fDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T02:50:14.592631Z","bundle_sha256":"cf6c737da135c22c45b7d15b0d1a493797aba6e39d6c055313537ae6bfbed047"}}