{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:QXOP7QGJV3HY7245H5G64M63B6","short_pith_number":"pith:QXOP7QGJ","schema_version":"1.0","canonical_sha256":"85dcffc0c9aecf8feb9d3f4dee33db0fa0a0751eef57d4b233d55bc829cb8986","source":{"kind":"arxiv","id":"2112.15311","version":1},"attestation_state":"computed","paper":{"title":"Bayesian Optimization of Function Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Peter I. Frazier, Raul Astudillo","submitted_at":"2021-12-31T05:35:21Z","abstract_excerpt":"We consider Bayesian optimization of the output of a network of functions, where each function takes as input the output of its parent nodes, and where the network takes significant time to evaluate. Such problems arise, for example, in reinforcement learning, engineering design, and manufacturing. While the standard Bayesian optimization approach observes only the final output, our approach delivers greater query efficiency by leveraging information that the former ignores: intermediate output within the network. This is achieved by modeling the nodes of the network using Gaussian processes a"},"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":"2112.15311","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-12-31T05:35:21Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"2659a311753c1c3ff35f7d54ecc9b09d911e09eec008651537ebc466eb45bf4d","abstract_canon_sha256":"7b78f69b46df0cae2c7c76e19bcdb237611d5c000fd03aff02dde1431cb2111c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:44:38.832044Z","signature_b64":"z2+THwMc2b1frXgvsGwhLVi1fopbxPONmZJa/dBL/cKeeITclB1JsqpjZDEfMEFIsLvhc6Hz85W0Yj7tnwzyDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"85dcffc0c9aecf8feb9d3f4dee33db0fa0a0751eef57d4b233d55bc829cb8986","last_reissued_at":"2026-07-05T03:44:38.831681Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:44:38.831681Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Optimization of Function Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Peter I. Frazier, Raul Astudillo","submitted_at":"2021-12-31T05:35:21Z","abstract_excerpt":"We consider Bayesian optimization of the output of a network of functions, where each function takes as input the output of its parent nodes, and where the network takes significant time to evaluate. Such problems arise, for example, in reinforcement learning, engineering design, and manufacturing. While the standard Bayesian optimization approach observes only the final output, our approach delivers greater query efficiency by leveraging information that the former ignores: intermediate output within the network. This is achieved by modeling the nodes of the network using Gaussian processes a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.15311","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2112.15311/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":"2112.15311","created_at":"2026-07-05T03:44:38.831744+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.15311v1","created_at":"2026-07-05T03:44:38.831744+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.15311","created_at":"2026-07-05T03:44:38.831744+00:00"},{"alias_kind":"pith_short_12","alias_value":"QXOP7QGJV3HY","created_at":"2026-07-05T03:44:38.831744+00:00"},{"alias_kind":"pith_short_16","alias_value":"QXOP7QGJV3HY7245","created_at":"2026-07-05T03:44:38.831744+00:00"},{"alias_kind":"pith_short_8","alias_value":"QXOP7QGJ","created_at":"2026-07-05T03:44:38.831744+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/QXOP7QGJV3HY7245H5G64M63B6","json":"https://pith.science/pith/QXOP7QGJV3HY7245H5G64M63B6.json","graph_json":"https://pith.science/api/pith-number/QXOP7QGJV3HY7245H5G64M63B6/graph.json","events_json":"https://pith.science/api/pith-number/QXOP7QGJV3HY7245H5G64M63B6/events.json","paper":"https://pith.science/paper/QXOP7QGJ"},"agent_actions":{"view_html":"https://pith.science/pith/QXOP7QGJV3HY7245H5G64M63B6","download_json":"https://pith.science/pith/QXOP7QGJV3HY7245H5G64M63B6.json","view_paper":"https://pith.science/paper/QXOP7QGJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.15311&json=true","fetch_graph":"https://pith.science/api/pith-number/QXOP7QGJV3HY7245H5G64M63B6/graph.json","fetch_events":"https://pith.science/api/pith-number/QXOP7QGJV3HY7245H5G64M63B6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QXOP7QGJV3HY7245H5G64M63B6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QXOP7QGJV3HY7245H5G64M63B6/action/storage_attestation","attest_author":"https://pith.science/pith/QXOP7QGJV3HY7245H5G64M63B6/action/author_attestation","sign_citation":"https://pith.science/pith/QXOP7QGJV3HY7245H5G64M63B6/action/citation_signature","submit_replication":"https://pith.science/pith/QXOP7QGJV3HY7245H5G64M63B6/action/replication_record"}},"created_at":"2026-07-05T03:44:38.831744+00:00","updated_at":"2026-07-05T03:44:38.831744+00:00"}