{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:33A4AAR5IFJ7IPY36VJ5DMV455","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":"618d5e1889beabd6e5559cdef93a93aecb27da0862270b64bc5781a4f1945a7e","cross_cats_sorted":["cs.LG","cs.NA","math.NA","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2020-06-09T16:04:00Z","title_canon_sha256":"9bf092e1d663b225509bf5ecc5d76ea8fc560905e7fa685e98a56af710a60a32"},"schema_version":"1.0","source":{"id":"2006.05371","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.05371","created_at":"2026-07-05T03:36:51Z"},{"alias_kind":"arxiv_version","alias_value":"2006.05371v3","created_at":"2026-07-05T03:36:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.05371","created_at":"2026-07-05T03:36:51Z"},{"alias_kind":"pith_short_12","alias_value":"33A4AAR5IFJ7","created_at":"2026-07-05T03:36:51Z"},{"alias_kind":"pith_short_16","alias_value":"33A4AAR5IFJ7IPY3","created_at":"2026-07-05T03:36:51Z"},{"alias_kind":"pith_short_8","alias_value":"33A4AAR5","created_at":"2026-07-05T03:36:51Z"}],"graph_snapshots":[{"event_id":"sha256:6fc87d6bd641082172de542089018eb4e1e921b8a59fcffb5d8ca37597f8374f","target":"graph","created_at":"2026-07-05T03:36:51Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2006.05371/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Bayesian quadrature (BQ) is a method for solving numerical integration problems in a Bayesian manner, which allows users to quantify their uncertainty about the solution. The standard approach to BQ is based on a Gaussian process (GP) approximation of the integrand. As a result, BQ is inherently limited to cases where GP approximations can be done in an efficient manner, thus often prohibiting very high-dimensional or non-smooth target functions. This paper proposes to tackle this issue with a new Bayesian numerical integration algorithm based on Bayesian Additive Regression Trees (BART) prior","authors_text":"Fran\\c{c}ois-Xavier Briol, Harrison Zhu, Ruya Kang, Seth Flaxman, Xing Liu, Zhichao Shen","cross_cats":["cs.LG","cs.NA","math.NA","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2020-06-09T16:04:00Z","title":"Bayesian Probabilistic Numerical Integration with Tree-Based Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.05371","kind":"arxiv","version":3},"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":"sha256:4ab3e417b9992b82a6846300a870d643c877743ff43047b66be2b1e842570674","target":"record","created_at":"2026-07-05T03:36:51Z","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":"618d5e1889beabd6e5559cdef93a93aecb27da0862270b64bc5781a4f1945a7e","cross_cats_sorted":["cs.LG","cs.NA","math.NA","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2020-06-09T16:04:00Z","title_canon_sha256":"9bf092e1d663b225509bf5ecc5d76ea8fc560905e7fa685e98a56af710a60a32"},"schema_version":"1.0","source":{"id":"2006.05371","kind":"arxiv","version":3}},"canonical_sha256":"dec1c0023d4153f43f1bf553d1b2bcef57879a870eae70ef419c8b84c3b2945a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dec1c0023d4153f43f1bf553d1b2bcef57879a870eae70ef419c8b84c3b2945a","first_computed_at":"2026-07-05T03:36:51.298030Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:36:51.298030Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RqU7dmzWHTa9SmsVEyhamXk3dUd88fsUqFotGFr/vRCWXDy6GLDTRO1uUTNni3GN/2OsZ2igM6J+xcUbPEMnDg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:36:51.298479Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.05371","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4ab3e417b9992b82a6846300a870d643c877743ff43047b66be2b1e842570674","sha256:6fc87d6bd641082172de542089018eb4e1e921b8a59fcffb5d8ca37597f8374f"],"state_sha256":"4bd73750109c7ed1883f32ed2b250eb82b70c5bdaa03c19cb566e50cb2f36ff9"}