{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:U22XAHF55WRXFW3HJXZBHMKOOR","short_pith_number":"pith:U22XAHF5","schema_version":"1.0","canonical_sha256":"a6b5701cbdeda372db674df213b14e7466858b00756ec2752119430f6629d072","source":{"kind":"arxiv","id":"2211.09678","version":1},"attestation_state":"computed","paper":{"title":"Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Anja Jankovic, Carola Doerr, Carolin Benjamins, Elena Raponi, Koen van der Blom, Marius Lindauer","submitted_at":"2022-11-17T17:15:04Z","abstract_excerpt":"Bayesian optimization (BO) algorithms form a class of surrogate-based heuristics, aimed at efficiently computing high-quality solutions for numerical black-box optimization problems. The BO pipeline is highly modular, with different design choices for the initial sampling strategy, the surrogate model, the acquisition function (AF), the solver used to optimize the AF, etc. We demonstrate in this work that a dynamic selection of the AF can benefit the BO design. More precisely, we show that already a na\\\"ive random forest regression model, built on top of exploratory landscape analysis features"},"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":"2211.09678","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-11-17T17:15:04Z","cross_cats_sorted":[],"title_canon_sha256":"37211361e4ef9a85d9d5eded35a09d561520f88270a92e416e200b06406765eb","abstract_canon_sha256":"19baa4df0a103fe7b139c5609e7c036dfba77244c059990373707307904c6210"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:17:04.954671Z","signature_b64":"6cTxJFWQiJf1NFNp05afCQzzTxVouVo60IML/MfSCBhX5uc7YIYqpeXgMFDOsFSe9AWwp1YFZ20CV2TijSVKAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6b5701cbdeda372db674df213b14e7466858b00756ec2752119430f6629d072","last_reissued_at":"2026-07-05T05:17:04.954264Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:17:04.954264Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Anja Jankovic, Carola Doerr, Carolin Benjamins, Elena Raponi, Koen van der Blom, Marius Lindauer","submitted_at":"2022-11-17T17:15:04Z","abstract_excerpt":"Bayesian optimization (BO) algorithms form a class of surrogate-based heuristics, aimed at efficiently computing high-quality solutions for numerical black-box optimization problems. The BO pipeline is highly modular, with different design choices for the initial sampling strategy, the surrogate model, the acquisition function (AF), the solver used to optimize the AF, etc. We demonstrate in this work that a dynamic selection of the AF can benefit the BO design. More precisely, we show that already a na\\\"ive random forest regression model, built on top of exploratory landscape analysis features"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.09678","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/2211.09678/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":"2211.09678","created_at":"2026-07-05T05:17:04.954321+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.09678v1","created_at":"2026-07-05T05:17:04.954321+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.09678","created_at":"2026-07-05T05:17:04.954321+00:00"},{"alias_kind":"pith_short_12","alias_value":"U22XAHF55WRX","created_at":"2026-07-05T05:17:04.954321+00:00"},{"alias_kind":"pith_short_16","alias_value":"U22XAHF55WRXFW3H","created_at":"2026-07-05T05:17:04.954321+00:00"},{"alias_kind":"pith_short_8","alias_value":"U22XAHF5","created_at":"2026-07-05T05:17:04.954321+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/U22XAHF55WRXFW3HJXZBHMKOOR","json":"https://pith.science/pith/U22XAHF55WRXFW3HJXZBHMKOOR.json","graph_json":"https://pith.science/api/pith-number/U22XAHF55WRXFW3HJXZBHMKOOR/graph.json","events_json":"https://pith.science/api/pith-number/U22XAHF55WRXFW3HJXZBHMKOOR/events.json","paper":"https://pith.science/paper/U22XAHF5"},"agent_actions":{"view_html":"https://pith.science/pith/U22XAHF55WRXFW3HJXZBHMKOOR","download_json":"https://pith.science/pith/U22XAHF55WRXFW3HJXZBHMKOOR.json","view_paper":"https://pith.science/paper/U22XAHF5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.09678&json=true","fetch_graph":"https://pith.science/api/pith-number/U22XAHF55WRXFW3HJXZBHMKOOR/graph.json","fetch_events":"https://pith.science/api/pith-number/U22XAHF55WRXFW3HJXZBHMKOOR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U22XAHF55WRXFW3HJXZBHMKOOR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U22XAHF55WRXFW3HJXZBHMKOOR/action/storage_attestation","attest_author":"https://pith.science/pith/U22XAHF55WRXFW3HJXZBHMKOOR/action/author_attestation","sign_citation":"https://pith.science/pith/U22XAHF55WRXFW3HJXZBHMKOOR/action/citation_signature","submit_replication":"https://pith.science/pith/U22XAHF55WRXFW3HJXZBHMKOOR/action/replication_record"}},"created_at":"2026-07-05T05:17:04.954321+00:00","updated_at":"2026-07-05T05:17:04.954321+00:00"}