{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HNBUC3PNABTD5IKB6GFLMTSCTH","short_pith_number":"pith:HNBUC3PN","schema_version":"1.0","canonical_sha256":"3b43416ded00663ea141f18ab64e4299d02a6b4a9af65a9b0b7c5ae9fd3baea5","source":{"kind":"arxiv","id":"2502.09198","version":2},"attestation_state":"computed","paper":{"title":"Understanding High-Dimensional Bayesian Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Leonard Papenmeier, Luigi Nardi, Matthias Poloczek","submitted_at":"2025-02-13T11:37:55Z","abstract_excerpt":"Recent work reported that simple Bayesian optimization (BO) methods perform well for high-dimensional real-world tasks, seemingly contradicting prior work and tribal knowledge. This paper investigates why. We identify underlying challenges that arise in high-dimensional BO and explain why recent methods succeed. Our empirical analysis shows that vanishing gradients caused by Gaussian process (GP) initialization schemes play a major role in the failures of high-dimensional Bayesian optimization (HDBO) and that methods that promote local search behaviors are better suited for the task. We find t"},"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":"2502.09198","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-13T11:37:55Z","cross_cats_sorted":[],"title_canon_sha256":"1138fcb5919b0ac14aac0a8b69d24e80e84550dc63fb3575d251ffca1b398e36","abstract_canon_sha256":"1e61505bedde9cfd4e2e7302126eee0b0a3c6cb87580a64992a249c5ca5ce492"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:01.506215Z","signature_b64":"lOA5vPLCezzccFEHFYFI56M3zzrpHHSS2RmViJFowpDrp46QoMuFZz57Lz39bhhufsjWjayMOxz8HeA0d/pUBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3b43416ded00663ea141f18ab64e4299d02a6b4a9af65a9b0b7c5ae9fd3baea5","last_reissued_at":"2026-05-17T23:39:01.505560Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:01.505560Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Understanding High-Dimensional Bayesian Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Leonard Papenmeier, Luigi Nardi, Matthias Poloczek","submitted_at":"2025-02-13T11:37:55Z","abstract_excerpt":"Recent work reported that simple Bayesian optimization (BO) methods perform well for high-dimensional real-world tasks, seemingly contradicting prior work and tribal knowledge. This paper investigates why. We identify underlying challenges that arise in high-dimensional BO and explain why recent methods succeed. Our empirical analysis shows that vanishing gradients caused by Gaussian process (GP) initialization schemes play a major role in the failures of high-dimensional Bayesian optimization (HDBO) and that methods that promote local search behaviors are better suited for the task. We find t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.09198","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"2502.09198","created_at":"2026-05-17T23:39:01.505657+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.09198v2","created_at":"2026-05-17T23:39:01.505657+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.09198","created_at":"2026-05-17T23:39:01.505657+00:00"},{"alias_kind":"pith_short_12","alias_value":"HNBUC3PNABTD","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"HNBUC3PNABTD5IKB","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"HNBUC3PN","created_at":"2026-05-18T12:33:37.589309+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2605.10654","citing_title":"Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights","ref_index":77,"is_internal_anchor":true},{"citing_arxiv_id":"2604.09417","citing_title":"Do We Really Need to Approach the Entire Pareto Front in Many-Objective Bayesian Optimisation?","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HNBUC3PNABTD5IKB6GFLMTSCTH","json":"https://pith.science/pith/HNBUC3PNABTD5IKB6GFLMTSCTH.json","graph_json":"https://pith.science/api/pith-number/HNBUC3PNABTD5IKB6GFLMTSCTH/graph.json","events_json":"https://pith.science/api/pith-number/HNBUC3PNABTD5IKB6GFLMTSCTH/events.json","paper":"https://pith.science/paper/HNBUC3PN"},"agent_actions":{"view_html":"https://pith.science/pith/HNBUC3PNABTD5IKB6GFLMTSCTH","download_json":"https://pith.science/pith/HNBUC3PNABTD5IKB6GFLMTSCTH.json","view_paper":"https://pith.science/paper/HNBUC3PN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.09198&json=true","fetch_graph":"https://pith.science/api/pith-number/HNBUC3PNABTD5IKB6GFLMTSCTH/graph.json","fetch_events":"https://pith.science/api/pith-number/HNBUC3PNABTD5IKB6GFLMTSCTH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HNBUC3PNABTD5IKB6GFLMTSCTH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HNBUC3PNABTD5IKB6GFLMTSCTH/action/storage_attestation","attest_author":"https://pith.science/pith/HNBUC3PNABTD5IKB6GFLMTSCTH/action/author_attestation","sign_citation":"https://pith.science/pith/HNBUC3PNABTD5IKB6GFLMTSCTH/action/citation_signature","submit_replication":"https://pith.science/pith/HNBUC3PNABTD5IKB6GFLMTSCTH/action/replication_record"}},"created_at":"2026-05-17T23:39:01.505657+00:00","updated_at":"2026-05-17T23:39:01.505657+00:00"}