{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:B2BXRRUPU4RPHKEKBGAI25P3O7","short_pith_number":"pith:B2BXRRUP","schema_version":"1.0","canonical_sha256":"0e8378c68fa722f3a88a09808d75fb77e76ec2b9061e1412df3dd7ce2cc8df8c","source":{"kind":"arxiv","id":"2410.09290","version":1},"attestation_state":"computed","paper":{"title":"Ranking over Regression for Bayesian Optimization and Molecule Selection","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Alan Aspuru-Guzik, Benjamin Sanchez-Lengeling, Gary Tom, Samantha Corapi, Stanley Lo","submitted_at":"2024-10-11T22:38:14Z","abstract_excerpt":"Bayesian optimization (BO) has become an indispensable tool for autonomous decision-making across diverse applications from autonomous vehicle control to accelerated drug and materials discovery. With the growing interest in self-driving laboratories, BO of chemical systems is crucial for machine learning (ML) guided experimental planning. Typically, BO employs a regression surrogate model to predict the distribution of unseen parts of the search space. However, for the selection of molecules, picking the top candidates with respect to a distribution, the relative ordering of their properties "},"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":"2410.09290","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-11T22:38:14Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"244fbb73542976a3418f837a860600c8440c7d852120fc412de39a0288b2e9fc","abstract_canon_sha256":"aee6a6b9381ec517a319f8abddf99432ad023b91c959581e03a61eb78201342d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:56:50.437860Z","signature_b64":"Vl3HsZfsRgKMjRBbAgsE2F6LlF8Y35tZF9vqI4loaNxJT/MZD+2HMXaNrOkkwfERlndsJ9/5y1nXsH11NjvkCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0e8378c68fa722f3a88a09808d75fb77e76ec2b9061e1412df3dd7ce2cc8df8c","last_reissued_at":"2026-07-05T11:56:50.437360Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:56:50.437360Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Ranking over Regression for Bayesian Optimization and Molecule Selection","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Alan Aspuru-Guzik, Benjamin Sanchez-Lengeling, Gary Tom, Samantha Corapi, Stanley Lo","submitted_at":"2024-10-11T22:38:14Z","abstract_excerpt":"Bayesian optimization (BO) has become an indispensable tool for autonomous decision-making across diverse applications from autonomous vehicle control to accelerated drug and materials discovery. With the growing interest in self-driving laboratories, BO of chemical systems is crucial for machine learning (ML) guided experimental planning. Typically, BO employs a regression surrogate model to predict the distribution of unseen parts of the search space. However, for the selection of molecules, picking the top candidates with respect to a distribution, the relative ordering of their properties "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.09290","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/2410.09290/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":"2410.09290","created_at":"2026-07-05T11:56:50.437422+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.09290v1","created_at":"2026-07-05T11:56:50.437422+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.09290","created_at":"2026-07-05T11:56:50.437422+00:00"},{"alias_kind":"pith_short_12","alias_value":"B2BXRRUPU4RP","created_at":"2026-07-05T11:56:50.437422+00:00"},{"alias_kind":"pith_short_16","alias_value":"B2BXRRUPU4RPHKEK","created_at":"2026-07-05T11:56:50.437422+00:00"},{"alias_kind":"pith_short_8","alias_value":"B2BXRRUP","created_at":"2026-07-05T11:56:50.437422+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/B2BXRRUPU4RPHKEKBGAI25P3O7","json":"https://pith.science/pith/B2BXRRUPU4RPHKEKBGAI25P3O7.json","graph_json":"https://pith.science/api/pith-number/B2BXRRUPU4RPHKEKBGAI25P3O7/graph.json","events_json":"https://pith.science/api/pith-number/B2BXRRUPU4RPHKEKBGAI25P3O7/events.json","paper":"https://pith.science/paper/B2BXRRUP"},"agent_actions":{"view_html":"https://pith.science/pith/B2BXRRUPU4RPHKEKBGAI25P3O7","download_json":"https://pith.science/pith/B2BXRRUPU4RPHKEKBGAI25P3O7.json","view_paper":"https://pith.science/paper/B2BXRRUP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.09290&json=true","fetch_graph":"https://pith.science/api/pith-number/B2BXRRUPU4RPHKEKBGAI25P3O7/graph.json","fetch_events":"https://pith.science/api/pith-number/B2BXRRUPU4RPHKEKBGAI25P3O7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B2BXRRUPU4RPHKEKBGAI25P3O7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B2BXRRUPU4RPHKEKBGAI25P3O7/action/storage_attestation","attest_author":"https://pith.science/pith/B2BXRRUPU4RPHKEKBGAI25P3O7/action/author_attestation","sign_citation":"https://pith.science/pith/B2BXRRUPU4RPHKEKBGAI25P3O7/action/citation_signature","submit_replication":"https://pith.science/pith/B2BXRRUPU4RPHKEKBGAI25P3O7/action/replication_record"}},"created_at":"2026-07-05T11:56:50.437422+00:00","updated_at":"2026-07-05T11:56:50.437422+00:00"}