{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:QMSBXHWC4QLAY7KWNIV4A3TEOZ","short_pith_number":"pith:QMSBXHWC","schema_version":"1.0","canonical_sha256":"83241b9ec2e4160c7d566a2bc06e64766c291b0f7a8d7a127a60894020ea8bb5","source":{"kind":"arxiv","id":"2102.01496","version":3},"attestation_state":"computed","paper":{"title":"Gaussian Experts Selection using Graphical Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Gjergji Kasneci, Hamed Jalali, Martin Pawelczyk","submitted_at":"2021-02-02T14:12:11Z","abstract_excerpt":"Local approximations are popular methods to scale Gaussian processes (GPs) to big data. Local approximations reduce time complexity by dividing the original dataset into subsets and training a local expert on each subset. Aggregating the experts' prediction is done assuming either conditional dependence or independence between the experts. Imposing the \\emph{conditional independence assumption} (CI) between the experts renders the aggregation of different expert predictions time efficient at the cost of poor uncertainty quantification. On the other hand, modeling dependent experts can provide "},"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":"2102.01496","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-02-02T14:12:11Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"54bdb423c2274ac64c458afd40badd7c2093c1b1185c3ec0fcd5d59a30bdf82f","abstract_canon_sha256":"00e1e62e8a5ce98394daf94bb72ed7f40de0179dab6de199c3903399599c22d9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:55:26.158287Z","signature_b64":"fmvrKjIJ0DYxqDB8qk7ycYKIY9k4XAvavhXKRiEF+rxQiCzxoARa+jVdtEWhz53p2MWp3ttWg0uAqtnxLGUFDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"83241b9ec2e4160c7d566a2bc06e64766c291b0f7a8d7a127a60894020ea8bb5","last_reissued_at":"2026-07-05T03:55:26.157653Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:55:26.157653Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gaussian Experts Selection using Graphical Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Gjergji Kasneci, Hamed Jalali, Martin Pawelczyk","submitted_at":"2021-02-02T14:12:11Z","abstract_excerpt":"Local approximations are popular methods to scale Gaussian processes (GPs) to big data. Local approximations reduce time complexity by dividing the original dataset into subsets and training a local expert on each subset. Aggregating the experts' prediction is done assuming either conditional dependence or independence between the experts. Imposing the \\emph{conditional independence assumption} (CI) between the experts renders the aggregation of different expert predictions time efficient at the cost of poor uncertainty quantification. On the other hand, modeling dependent experts can provide "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.01496","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/2102.01496/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":"2102.01496","created_at":"2026-07-05T03:55:26.157815+00:00"},{"alias_kind":"arxiv_version","alias_value":"2102.01496v3","created_at":"2026-07-05T03:55:26.157815+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.01496","created_at":"2026-07-05T03:55:26.157815+00:00"},{"alias_kind":"pith_short_12","alias_value":"QMSBXHWC4QLA","created_at":"2026-07-05T03:55:26.157815+00:00"},{"alias_kind":"pith_short_16","alias_value":"QMSBXHWC4QLAY7KW","created_at":"2026-07-05T03:55:26.157815+00:00"},{"alias_kind":"pith_short_8","alias_value":"QMSBXHWC","created_at":"2026-07-05T03:55:26.157815+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/QMSBXHWC4QLAY7KWNIV4A3TEOZ","json":"https://pith.science/pith/QMSBXHWC4QLAY7KWNIV4A3TEOZ.json","graph_json":"https://pith.science/api/pith-number/QMSBXHWC4QLAY7KWNIV4A3TEOZ/graph.json","events_json":"https://pith.science/api/pith-number/QMSBXHWC4QLAY7KWNIV4A3TEOZ/events.json","paper":"https://pith.science/paper/QMSBXHWC"},"agent_actions":{"view_html":"https://pith.science/pith/QMSBXHWC4QLAY7KWNIV4A3TEOZ","download_json":"https://pith.science/pith/QMSBXHWC4QLAY7KWNIV4A3TEOZ.json","view_paper":"https://pith.science/paper/QMSBXHWC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2102.01496&json=true","fetch_graph":"https://pith.science/api/pith-number/QMSBXHWC4QLAY7KWNIV4A3TEOZ/graph.json","fetch_events":"https://pith.science/api/pith-number/QMSBXHWC4QLAY7KWNIV4A3TEOZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QMSBXHWC4QLAY7KWNIV4A3TEOZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QMSBXHWC4QLAY7KWNIV4A3TEOZ/action/storage_attestation","attest_author":"https://pith.science/pith/QMSBXHWC4QLAY7KWNIV4A3TEOZ/action/author_attestation","sign_citation":"https://pith.science/pith/QMSBXHWC4QLAY7KWNIV4A3TEOZ/action/citation_signature","submit_replication":"https://pith.science/pith/QMSBXHWC4QLAY7KWNIV4A3TEOZ/action/replication_record"}},"created_at":"2026-07-05T03:55:26.157815+00:00","updated_at":"2026-07-05T03:55:26.157815+00:00"}