{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:BJ5VIXH3CUN7UU4AAERCKVAZ7J","short_pith_number":"pith:BJ5VIXH3","schema_version":"1.0","canonical_sha256":"0a7b545cfb151bfa53800122255419fa7c7917b185dc7d8322468a99550aaf6b","source":{"kind":"arxiv","id":"1910.07003","version":1},"attestation_state":"computed","paper":{"title":"Constrained Bayesian Optimization with Max-Value Entropy Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Cedric Archambeau, Iaroslav Shcherbatyi, Matthias Seeger, Rodolphe Jenatton, Valerio Perrone","submitted_at":"2019-10-15T18:56:04Z","abstract_excerpt":"Bayesian optimization (BO) is a model-based approach to sequentially optimize expensive black-box functions, such as the validation error of a deep neural network with respect to its hyperparameters. In many real-world scenarios, the optimization is further subject to a priori unknown constraints. For example, training a deep network configuration may fail with an out-of-memory error when the model is too large. In this work, we focus on a general formulation of Gaussian process-based BO with continuous or binary constraints. We propose constrained Max-value Entropy Search (cMES), a novel info"},"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":"1910.07003","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-10-15T18:56:04Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e098e3ef4954a91cf33748646de701bdf9ae8919dd3974ee4e2d7749ac7c7781","abstract_canon_sha256":"8d65d2b4613085e6a72e41933ef819b1fa2edba63ea2075bab18c5d982b49a0b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:12:31.771571Z","signature_b64":"nMBUED85RLYRZlMBS+Pn05wCco6yePPRMHoJpGiaRHrxOV64QeOy4sgtJBkkEaUGho44mEhZFJm5eVYr7gN+Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a7b545cfb151bfa53800122255419fa7c7917b185dc7d8322468a99550aaf6b","last_reissued_at":"2026-07-05T00:12:31.771189Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:12:31.771189Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Constrained Bayesian Optimization with Max-Value Entropy Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Cedric Archambeau, Iaroslav Shcherbatyi, Matthias Seeger, Rodolphe Jenatton, Valerio Perrone","submitted_at":"2019-10-15T18:56:04Z","abstract_excerpt":"Bayesian optimization (BO) is a model-based approach to sequentially optimize expensive black-box functions, such as the validation error of a deep neural network with respect to its hyperparameters. In many real-world scenarios, the optimization is further subject to a priori unknown constraints. For example, training a deep network configuration may fail with an out-of-memory error when the model is too large. In this work, we focus on a general formulation of Gaussian process-based BO with continuous or binary constraints. We propose constrained Max-value Entropy Search (cMES), a novel info"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.07003","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/1910.07003/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":"1910.07003","created_at":"2026-07-05T00:12:31.771245+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.07003v1","created_at":"2026-07-05T00:12:31.771245+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.07003","created_at":"2026-07-05T00:12:31.771245+00:00"},{"alias_kind":"pith_short_12","alias_value":"BJ5VIXH3CUN7","created_at":"2026-07-05T00:12:31.771245+00:00"},{"alias_kind":"pith_short_16","alias_value":"BJ5VIXH3CUN7UU4A","created_at":"2026-07-05T00:12:31.771245+00:00"},{"alias_kind":"pith_short_8","alias_value":"BJ5VIXH3","created_at":"2026-07-05T00:12:31.771245+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.00865","citing_title":"Constrained Bayesian Optimisation with Multiple Information Sources","ref_index":46,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BJ5VIXH3CUN7UU4AAERCKVAZ7J","json":"https://pith.science/pith/BJ5VIXH3CUN7UU4AAERCKVAZ7J.json","graph_json":"https://pith.science/api/pith-number/BJ5VIXH3CUN7UU4AAERCKVAZ7J/graph.json","events_json":"https://pith.science/api/pith-number/BJ5VIXH3CUN7UU4AAERCKVAZ7J/events.json","paper":"https://pith.science/paper/BJ5VIXH3"},"agent_actions":{"view_html":"https://pith.science/pith/BJ5VIXH3CUN7UU4AAERCKVAZ7J","download_json":"https://pith.science/pith/BJ5VIXH3CUN7UU4AAERCKVAZ7J.json","view_paper":"https://pith.science/paper/BJ5VIXH3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.07003&json=true","fetch_graph":"https://pith.science/api/pith-number/BJ5VIXH3CUN7UU4AAERCKVAZ7J/graph.json","fetch_events":"https://pith.science/api/pith-number/BJ5VIXH3CUN7UU4AAERCKVAZ7J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BJ5VIXH3CUN7UU4AAERCKVAZ7J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BJ5VIXH3CUN7UU4AAERCKVAZ7J/action/storage_attestation","attest_author":"https://pith.science/pith/BJ5VIXH3CUN7UU4AAERCKVAZ7J/action/author_attestation","sign_citation":"https://pith.science/pith/BJ5VIXH3CUN7UU4AAERCKVAZ7J/action/citation_signature","submit_replication":"https://pith.science/pith/BJ5VIXH3CUN7UU4AAERCKVAZ7J/action/replication_record"}},"created_at":"2026-07-05T00:12:31.771245+00:00","updated_at":"2026-07-05T00:12:31.771245+00:00"}