{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:URSMHOLTWBC5LU7CU63S34C452","short_pith_number":"pith:URSMHOLT","schema_version":"1.0","canonical_sha256":"a464c3b973b045d5d3e2a7b72df05cee81cdb33ee772b020aeb8b827a77ef9b6","source":{"kind":"arxiv","id":"2006.05468","version":3},"attestation_state":"computed","paper":{"title":"Variational Auto-Regressive Gaussian Processes for Continual Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Sanyam Kapoor, Thang D. Bui, Theofanis Karaletsos","submitted_at":"2020-06-09T19:23:57Z","abstract_excerpt":"Through sequential construction of posteriors on observing data online, Bayes' theorem provides a natural framework for continual learning. We develop Variational Auto-Regressive Gaussian Processes (VAR-GPs), a principled posterior updating mechanism to solve sequential tasks in continual learning. By relying on sparse inducing point approximations for scalable posteriors, we propose a novel auto-regressive variational distribution which reveals two fruitful connections to existing results in Bayesian inference, expectation propagation and orthogonal inducing points. Mean predictive entropy es"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2006.05468","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-06-09T19:23:57Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"90c08bae29ee0eae53dfa61d40f7712390d532210cd18863052a6482151ef535","abstract_canon_sha256":"64d57fafc275f1ebc7632015e2a9132fc6483590557a30b375ee30b9a504f1a6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:48:33.388845Z","signature_b64":"u9vaiUYy2cqoaaxfIG+yhnkGnEXXScYB3l6uDNhqbX0I82CksOSWxUgyCe0823TedmkK8rc5sGHO1oiTlc4HBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a464c3b973b045d5d3e2a7b72df05cee81cdb33ee772b020aeb8b827a77ef9b6","last_reissued_at":"2026-07-05T02:48:33.388362Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:48:33.388362Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Variational Auto-Regressive Gaussian Processes for Continual Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Sanyam Kapoor, Thang D. Bui, Theofanis Karaletsos","submitted_at":"2020-06-09T19:23:57Z","abstract_excerpt":"Through sequential construction of posteriors on observing data online, Bayes' theorem provides a natural framework for continual learning. We develop Variational Auto-Regressive Gaussian Processes (VAR-GPs), a principled posterior updating mechanism to solve sequential tasks in continual learning. By relying on sparse inducing point approximations for scalable posteriors, we propose a novel auto-regressive variational distribution which reveals two fruitful connections to existing results in Bayesian inference, expectation propagation and orthogonal inducing points. Mean predictive entropy es"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.05468","kind":"arxiv","version":3},"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/2006.05468/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2006.05468","created_at":"2026-07-05T02:48:33.388425+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.05468v3","created_at":"2026-07-05T02:48:33.388425+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.05468","created_at":"2026-07-05T02:48:33.388425+00:00"},{"alias_kind":"pith_short_12","alias_value":"URSMHOLTWBC5","created_at":"2026-07-05T02:48:33.388425+00:00"},{"alias_kind":"pith_short_16","alias_value":"URSMHOLTWBC5LU7C","created_at":"2026-07-05T02:48:33.388425+00:00"},{"alias_kind":"pith_short_8","alias_value":"URSMHOLT","created_at":"2026-07-05T02:48:33.388425+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/URSMHOLTWBC5LU7CU63S34C452","json":"https://pith.science/pith/URSMHOLTWBC5LU7CU63S34C452.json","graph_json":"https://pith.science/api/pith-number/URSMHOLTWBC5LU7CU63S34C452/graph.json","events_json":"https://pith.science/api/pith-number/URSMHOLTWBC5LU7CU63S34C452/events.json","paper":"https://pith.science/paper/URSMHOLT"},"agent_actions":{"view_html":"https://pith.science/pith/URSMHOLTWBC5LU7CU63S34C452","download_json":"https://pith.science/pith/URSMHOLTWBC5LU7CU63S34C452.json","view_paper":"https://pith.science/paper/URSMHOLT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.05468&json=true","fetch_graph":"https://pith.science/api/pith-number/URSMHOLTWBC5LU7CU63S34C452/graph.json","fetch_events":"https://pith.science/api/pith-number/URSMHOLTWBC5LU7CU63S34C452/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/URSMHOLTWBC5LU7CU63S34C452/action/timestamp_anchor","attest_storage":"https://pith.science/pith/URSMHOLTWBC5LU7CU63S34C452/action/storage_attestation","attest_author":"https://pith.science/pith/URSMHOLTWBC5LU7CU63S34C452/action/author_attestation","sign_citation":"https://pith.science/pith/URSMHOLTWBC5LU7CU63S34C452/action/citation_signature","submit_replication":"https://pith.science/pith/URSMHOLTWBC5LU7CU63S34C452/action/replication_record"}},"created_at":"2026-07-05T02:48:33.388425+00:00","updated_at":"2026-07-05T02:48:33.388425+00:00"}