{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:GJNDHZGAIMURYIDWOZQEKYZXZH","short_pith_number":"pith:GJNDHZGA","schema_version":"1.0","canonical_sha256":"325a33e4c043291c20767660456337c9e8d4166cf884c5765ac070c944c17758","source":{"kind":"arxiv","id":"1906.11152","version":4},"attestation_state":"computed","paper":{"title":"Modulating Surrogates for Bayesian Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Carl Henrik Ek, Erik Bodin, Ieva Kazlauskaite, Markus Kaiser, Neill D. F. Campbell, Zhenwen Dai","submitted_at":"2019-06-26T15:10:44Z","abstract_excerpt":"Bayesian optimization (BO) methods often rely on the assumption that the objective function is well-behaved, but in practice, this is seldom true for real-world objectives even if noise-free observations can be collected. Common approaches, which try to model the objective as precisely as possible, often fail to make progress by spending too many evaluations modeling irrelevant details. We address this issue by proposing surrogate models that focus on the well-behaved structure in the objective function, which is informative for search, while ignoring detrimental structure that is challenging "},"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":"1906.11152","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-06-26T15:10:44Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"38486d4dd9ddac6c5468b91d462253651729d1787683874fded5c989eeae4da3","abstract_canon_sha256":"a87dced58f1f2d40c88296a9f7eaad4e4ad070a319097badf4e514059af5186e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:34:04.456683Z","signature_b64":"1h+90fyXcWjwULb/jwFhlp4Ll9tTn6iy0l6cdLU7ZbXPbICxlgGHMlTrskr5Ku2sJfSiJMKPhnV/V/RmHIDgCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"325a33e4c043291c20767660456337c9e8d4166cf884c5765ac070c944c17758","last_reissued_at":"2026-07-05T01:34:04.456174Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:34:04.456174Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Modulating Surrogates for Bayesian Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Carl Henrik Ek, Erik Bodin, Ieva Kazlauskaite, Markus Kaiser, Neill D. F. Campbell, Zhenwen Dai","submitted_at":"2019-06-26T15:10:44Z","abstract_excerpt":"Bayesian optimization (BO) methods often rely on the assumption that the objective function is well-behaved, but in practice, this is seldom true for real-world objectives even if noise-free observations can be collected. Common approaches, which try to model the objective as precisely as possible, often fail to make progress by spending too many evaluations modeling irrelevant details. We address this issue by proposing surrogate models that focus on the well-behaved structure in the objective function, which is informative for search, while ignoring detrimental structure that is challenging "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.11152","kind":"arxiv","version":4},"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/1906.11152/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":"1906.11152","created_at":"2026-07-05T01:34:04.456241+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.11152v4","created_at":"2026-07-05T01:34:04.456241+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.11152","created_at":"2026-07-05T01:34:04.456241+00:00"},{"alias_kind":"pith_short_12","alias_value":"GJNDHZGAIMUR","created_at":"2026-07-05T01:34:04.456241+00:00"},{"alias_kind":"pith_short_16","alias_value":"GJNDHZGAIMURYIDW","created_at":"2026-07-05T01:34:04.456241+00:00"},{"alias_kind":"pith_short_8","alias_value":"GJNDHZGA","created_at":"2026-07-05T01:34:04.456241+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/GJNDHZGAIMURYIDWOZQEKYZXZH","json":"https://pith.science/pith/GJNDHZGAIMURYIDWOZQEKYZXZH.json","graph_json":"https://pith.science/api/pith-number/GJNDHZGAIMURYIDWOZQEKYZXZH/graph.json","events_json":"https://pith.science/api/pith-number/GJNDHZGAIMURYIDWOZQEKYZXZH/events.json","paper":"https://pith.science/paper/GJNDHZGA"},"agent_actions":{"view_html":"https://pith.science/pith/GJNDHZGAIMURYIDWOZQEKYZXZH","download_json":"https://pith.science/pith/GJNDHZGAIMURYIDWOZQEKYZXZH.json","view_paper":"https://pith.science/paper/GJNDHZGA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.11152&json=true","fetch_graph":"https://pith.science/api/pith-number/GJNDHZGAIMURYIDWOZQEKYZXZH/graph.json","fetch_events":"https://pith.science/api/pith-number/GJNDHZGAIMURYIDWOZQEKYZXZH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GJNDHZGAIMURYIDWOZQEKYZXZH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GJNDHZGAIMURYIDWOZQEKYZXZH/action/storage_attestation","attest_author":"https://pith.science/pith/GJNDHZGAIMURYIDWOZQEKYZXZH/action/author_attestation","sign_citation":"https://pith.science/pith/GJNDHZGAIMURYIDWOZQEKYZXZH/action/citation_signature","submit_replication":"https://pith.science/pith/GJNDHZGAIMURYIDWOZQEKYZXZH/action/replication_record"}},"created_at":"2026-07-05T01:34:04.456241+00:00","updated_at":"2026-07-05T01:34:04.456241+00:00"}