{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:GHPVJZIKYMLU3BPTNFREOQCQTU","short_pith_number":"pith:GHPVJZIK","schema_version":"1.0","canonical_sha256":"31df54e50ac3174d85f369624740509d2293edc85258568ff3cfa1f132e28b88","source":{"kind":"arxiv","id":"2306.09803","version":3},"attestation_state":"computed","paper":{"title":"Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Antoine Grosnit, Haitham Bou Ammar, Kamil Dreczkowski","submitted_at":"2023-06-16T12:38:33Z","abstract_excerpt":"This paper introduces a modular framework for Mixed-variable and Combinatorial Bayesian Optimization (MCBO) to address the lack of systematic benchmarking and standardized evaluation in the field. Current MCBO papers often introduce non-diverse or non-standard benchmarks to evaluate their methods, impeding the proper assessment of different MCBO primitives and their combinations. Additionally, papers introducing a solution for a single MCBO primitive often omit benchmarking against baselines that utilize the same methods for the remaining primitives. This omission is primarily due to the signi"},"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":"2306.09803","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-16T12:38:33Z","cross_cats_sorted":[],"title_canon_sha256":"1eefbef5b5f47b09c578a82a8343fa21a28b6638266e60c447383ecd26f358fb","abstract_canon_sha256":"7e0d828059d2c5d4778a7a718c5ef52515dcae04e2e48ebeb6f87564e7212f17"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:22:19.618641Z","signature_b64":"KmE6ReruMsgOftbleJYfWzupvKwGXx3l7QuUHLHiY4JbsmBKQm+N7PxoUaZH/gJV5n4Ob33Gm2+9FaMjEkBZAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31df54e50ac3174d85f369624740509d2293edc85258568ff3cfa1f132e28b88","last_reissued_at":"2026-07-05T07:22:19.618110Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:22:19.618110Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Antoine Grosnit, Haitham Bou Ammar, Kamil Dreczkowski","submitted_at":"2023-06-16T12:38:33Z","abstract_excerpt":"This paper introduces a modular framework for Mixed-variable and Combinatorial Bayesian Optimization (MCBO) to address the lack of systematic benchmarking and standardized evaluation in the field. Current MCBO papers often introduce non-diverse or non-standard benchmarks to evaluate their methods, impeding the proper assessment of different MCBO primitives and their combinations. Additionally, papers introducing a solution for a single MCBO primitive often omit benchmarking against baselines that utilize the same methods for the remaining primitives. This omission is primarily due to the signi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.09803","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/2306.09803/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":"2306.09803","created_at":"2026-07-05T07:22:19.618169+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.09803v3","created_at":"2026-07-05T07:22:19.618169+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.09803","created_at":"2026-07-05T07:22:19.618169+00:00"},{"alias_kind":"pith_short_12","alias_value":"GHPVJZIKYMLU","created_at":"2026-07-05T07:22:19.618169+00:00"},{"alias_kind":"pith_short_16","alias_value":"GHPVJZIKYMLU3BPT","created_at":"2026-07-05T07:22:19.618169+00:00"},{"alias_kind":"pith_short_8","alias_value":"GHPVJZIK","created_at":"2026-07-05T07:22:19.618169+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.07416","citing_title":"Bayesian Optimization for Mixed-Variable Problems in the Natural Sciences","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GHPVJZIKYMLU3BPTNFREOQCQTU","json":"https://pith.science/pith/GHPVJZIKYMLU3BPTNFREOQCQTU.json","graph_json":"https://pith.science/api/pith-number/GHPVJZIKYMLU3BPTNFREOQCQTU/graph.json","events_json":"https://pith.science/api/pith-number/GHPVJZIKYMLU3BPTNFREOQCQTU/events.json","paper":"https://pith.science/paper/GHPVJZIK"},"agent_actions":{"view_html":"https://pith.science/pith/GHPVJZIKYMLU3BPTNFREOQCQTU","download_json":"https://pith.science/pith/GHPVJZIKYMLU3BPTNFREOQCQTU.json","view_paper":"https://pith.science/paper/GHPVJZIK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.09803&json=true","fetch_graph":"https://pith.science/api/pith-number/GHPVJZIKYMLU3BPTNFREOQCQTU/graph.json","fetch_events":"https://pith.science/api/pith-number/GHPVJZIKYMLU3BPTNFREOQCQTU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GHPVJZIKYMLU3BPTNFREOQCQTU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GHPVJZIKYMLU3BPTNFREOQCQTU/action/storage_attestation","attest_author":"https://pith.science/pith/GHPVJZIKYMLU3BPTNFREOQCQTU/action/author_attestation","sign_citation":"https://pith.science/pith/GHPVJZIKYMLU3BPTNFREOQCQTU/action/citation_signature","submit_replication":"https://pith.science/pith/GHPVJZIKYMLU3BPTNFREOQCQTU/action/replication_record"}},"created_at":"2026-07-05T07:22:19.618169+00:00","updated_at":"2026-07-05T07:22:19.618169+00:00"}