{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:Y3ES2KGWWNQXL6FQEQG5TP6RAK","short_pith_number":"pith:Y3ES2KGW","schema_version":"1.0","canonical_sha256":"c6c92d28d6b36175f8b0240dd9bfd102811913fe11d8ae31393dea39dc332f0b","source":{"kind":"arxiv","id":"1905.03350","version":1},"attestation_state":"computed","paper":{"title":"Bayesian Optimization using Deep Gaussian Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE","cs.LG"],"primary_cat":"stat.ML","authors_text":"Ali Hebbal, El-Ghazali Talbi, Loic Brevault, Mathieu Balesdent, Nouredine Melab","submitted_at":"2019-05-07T11:07:53Z","abstract_excerpt":"Bayesian Optimization using Gaussian Processes is a popular approach to deal with the optimization of expensive black-box functions. However, because of the a priori on the stationarity of the covariance matrix of classic Gaussian Processes, this method may not be adapted for non-stationary functions involved in the optimization problem. To overcome this issue, a new Bayesian Optimization approach is proposed. It is based on Deep Gaussian Processes as surrogate models instead of classic Gaussian Processes. This modeling technique increases the power of representation to capture the non-station"},"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":"1905.03350","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-05-07T11:07:53Z","cross_cats_sorted":["cs.CE","cs.LG"],"title_canon_sha256":"113bddba4e2d817fb8307ffa71e8be8bcb477a820a922706d4bce83a506b3b17","abstract_canon_sha256":"3e053818fb382418306fc81c73bffa271082ea4566cadc9f8820bb7077cdacf2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:40.279747Z","signature_b64":"KK5ppBZV8FKsmIXQTEaLlIZLxPs5tgWG5QBjExe8Ak8y9pqcpc5w6agdxJzNpJIkWbmxuiV/G+yZJd4FWUvfAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6c92d28d6b36175f8b0240dd9bfd102811913fe11d8ae31393dea39dc332f0b","last_reissued_at":"2026-05-17T23:46:40.279180Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:40.279180Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Optimization using Deep Gaussian Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE","cs.LG"],"primary_cat":"stat.ML","authors_text":"Ali Hebbal, El-Ghazali Talbi, Loic Brevault, Mathieu Balesdent, Nouredine Melab","submitted_at":"2019-05-07T11:07:53Z","abstract_excerpt":"Bayesian Optimization using Gaussian Processes is a popular approach to deal with the optimization of expensive black-box functions. However, because of the a priori on the stationarity of the covariance matrix of classic Gaussian Processes, this method may not be adapted for non-stationary functions involved in the optimization problem. To overcome this issue, a new Bayesian Optimization approach is proposed. It is based on Deep Gaussian Processes as surrogate models instead of classic Gaussian Processes. This modeling technique increases the power of representation to capture the non-station"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.03350","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":""},"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":"1905.03350","created_at":"2026-05-17T23:46:40.279273+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.03350v1","created_at":"2026-05-17T23:46:40.279273+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.03350","created_at":"2026-05-17T23:46:40.279273+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y3ES2KGWWNQX","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y3ES2KGWWNQXL6FQ","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y3ES2KGW","created_at":"2026-05-18T12:33:33.725879+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.10669","citing_title":"Modernizing HEBO: a robust Bayesian optimization baseline for practical heteroskedastic and non-stationary problems","ref_index":42,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Y3ES2KGWWNQXL6FQEQG5TP6RAK","json":"https://pith.science/pith/Y3ES2KGWWNQXL6FQEQG5TP6RAK.json","graph_json":"https://pith.science/api/pith-number/Y3ES2KGWWNQXL6FQEQG5TP6RAK/graph.json","events_json":"https://pith.science/api/pith-number/Y3ES2KGWWNQXL6FQEQG5TP6RAK/events.json","paper":"https://pith.science/paper/Y3ES2KGW"},"agent_actions":{"view_html":"https://pith.science/pith/Y3ES2KGWWNQXL6FQEQG5TP6RAK","download_json":"https://pith.science/pith/Y3ES2KGWWNQXL6FQEQG5TP6RAK.json","view_paper":"https://pith.science/paper/Y3ES2KGW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.03350&json=true","fetch_graph":"https://pith.science/api/pith-number/Y3ES2KGWWNQXL6FQEQG5TP6RAK/graph.json","fetch_events":"https://pith.science/api/pith-number/Y3ES2KGWWNQXL6FQEQG5TP6RAK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y3ES2KGWWNQXL6FQEQG5TP6RAK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y3ES2KGWWNQXL6FQEQG5TP6RAK/action/storage_attestation","attest_author":"https://pith.science/pith/Y3ES2KGWWNQXL6FQEQG5TP6RAK/action/author_attestation","sign_citation":"https://pith.science/pith/Y3ES2KGWWNQXL6FQEQG5TP6RAK/action/citation_signature","submit_replication":"https://pith.science/pith/Y3ES2KGWWNQXL6FQEQG5TP6RAK/action/replication_record"}},"created_at":"2026-05-17T23:46:40.279273+00:00","updated_at":"2026-05-17T23:46:40.279273+00:00"}