{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PJXFONFSH43K75YMJ63VP62B7J","short_pith_number":"pith:PJXFONFS","schema_version":"1.0","canonical_sha256":"7a6e5734b23f36aff70c4fb757fb41fa67c346d6c74f2a48e5646baae4572f7a","source":{"kind":"arxiv","id":"2406.20062","version":3},"attestation_state":"computed","paper":{"title":"Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexander Terenin, Peter I. Frazier, Qian Xie, Raul Astudillo, Ziv Scully","submitted_at":"2024-06-28T17:20:13Z","abstract_excerpt":"Bayesian optimization is a technique for efficiently optimizing unknown functions in a black-box manner. To handle practical settings where gathering data requires use of finite resources, it is desirable to explicitly incorporate function evaluation costs into Bayesian optimization policies. To understand how to do so, we develop a previously-unexplored connection between cost-aware Bayesian optimization and the Pandora's Box problem, a decision problem from economics. The Pandora's Box problem admits a Bayesian-optimal solution based on an expression called the Gittins index, which can be re"},"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":"2406.20062","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-28T17:20:13Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"07daaaff2a2edabe4ab1ff3a39bbfe425bcb7ab7a592fe437cb0ac558979c602","abstract_canon_sha256":"4e364b761f737d10c4fd88bfcd71dfededc071e0758379e86ce45079e0177142"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:01:34.442562Z","signature_b64":"ErCi2osADdbLCRVynbVfVJt6HBt3cePUmJHcAQkWEa9mn75BCuBM9RG165LJt0QqmgwGMrGxNbf+kMo+ZeL6Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a6e5734b23f36aff70c4fb757fb41fa67c346d6c74f2a48e5646baae4572f7a","last_reissued_at":"2026-07-05T10:01:34.442121Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:01:34.442121Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexander Terenin, Peter I. Frazier, Qian Xie, Raul Astudillo, Ziv Scully","submitted_at":"2024-06-28T17:20:13Z","abstract_excerpt":"Bayesian optimization is a technique for efficiently optimizing unknown functions in a black-box manner. To handle practical settings where gathering data requires use of finite resources, it is desirable to explicitly incorporate function evaluation costs into Bayesian optimization policies. To understand how to do so, we develop a previously-unexplored connection between cost-aware Bayesian optimization and the Pandora's Box problem, a decision problem from economics. The Pandora's Box problem admits a Bayesian-optimal solution based on an expression called the Gittins index, which can be re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.20062","kind":"arxiv","version":3},"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/2406.20062/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":"2406.20062","created_at":"2026-07-05T10:01:34.442176+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.20062v3","created_at":"2026-07-05T10:01:34.442176+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.20062","created_at":"2026-07-05T10:01:34.442176+00:00"},{"alias_kind":"pith_short_12","alias_value":"PJXFONFSH43K","created_at":"2026-07-05T10:01:34.442176+00:00"},{"alias_kind":"pith_short_16","alias_value":"PJXFONFSH43K75YM","created_at":"2026-07-05T10:01:34.442176+00:00"},{"alias_kind":"pith_short_8","alias_value":"PJXFONFS","created_at":"2026-07-05T10:01:34.442176+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.10872","citing_title":"The Gittins Index: A Design Principle for Decision-Making Under Uncertainty","ref_index":114,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PJXFONFSH43K75YMJ63VP62B7J","json":"https://pith.science/pith/PJXFONFSH43K75YMJ63VP62B7J.json","graph_json":"https://pith.science/api/pith-number/PJXFONFSH43K75YMJ63VP62B7J/graph.json","events_json":"https://pith.science/api/pith-number/PJXFONFSH43K75YMJ63VP62B7J/events.json","paper":"https://pith.science/paper/PJXFONFS"},"agent_actions":{"view_html":"https://pith.science/pith/PJXFONFSH43K75YMJ63VP62B7J","download_json":"https://pith.science/pith/PJXFONFSH43K75YMJ63VP62B7J.json","view_paper":"https://pith.science/paper/PJXFONFS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.20062&json=true","fetch_graph":"https://pith.science/api/pith-number/PJXFONFSH43K75YMJ63VP62B7J/graph.json","fetch_events":"https://pith.science/api/pith-number/PJXFONFSH43K75YMJ63VP62B7J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PJXFONFSH43K75YMJ63VP62B7J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PJXFONFSH43K75YMJ63VP62B7J/action/storage_attestation","attest_author":"https://pith.science/pith/PJXFONFSH43K75YMJ63VP62B7J/action/author_attestation","sign_citation":"https://pith.science/pith/PJXFONFSH43K75YMJ63VP62B7J/action/citation_signature","submit_replication":"https://pith.science/pith/PJXFONFSH43K75YMJ63VP62B7J/action/replication_record"}},"created_at":"2026-07-05T10:01:34.442176+00:00","updated_at":"2026-07-05T10:01:34.442176+00:00"}