{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:R6NDN3NK35LPUE3DM23VLTQGMV","short_pith_number":"pith:R6NDN3NK","canonical_record":{"source":{"id":"2502.00854","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"math.OC","submitted_at":"2025-02-02T16:57:05Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"2e1a90b1937f180443148457eaf44cf93e4849f6e670dde97daf7402154ef2a7","abstract_canon_sha256":"0b77e94af603e19ea3b31882663416a1dacf96d2b347fde2d4a78288d9da4b0e"},"schema_version":"1.0"},"canonical_sha256":"8f9a36edaadf56fa136366b755ce06656a7f8f9d03683629ada8def3130c0ad4","source":{"kind":"arxiv","id":"2502.00854","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.00854","created_at":"2026-07-05T10:10:48Z"},{"alias_kind":"arxiv_version","alias_value":"2502.00854v1","created_at":"2026-07-05T10:10:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.00854","created_at":"2026-07-05T10:10:48Z"},{"alias_kind":"pith_short_12","alias_value":"R6NDN3NK35LP","created_at":"2026-07-05T10:10:48Z"},{"alias_kind":"pith_short_16","alias_value":"R6NDN3NK35LPUE3D","created_at":"2026-07-05T10:10:48Z"},{"alias_kind":"pith_short_8","alias_value":"R6NDN3NK","created_at":"2026-07-05T10:10:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:R6NDN3NK35LPUE3DM23VLTQGMV","target":"record","payload":{"canonical_record":{"source":{"id":"2502.00854","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"math.OC","submitted_at":"2025-02-02T16:57:05Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"2e1a90b1937f180443148457eaf44cf93e4849f6e670dde97daf7402154ef2a7","abstract_canon_sha256":"0b77e94af603e19ea3b31882663416a1dacf96d2b347fde2d4a78288d9da4b0e"},"schema_version":"1.0"},"canonical_sha256":"8f9a36edaadf56fa136366b755ce06656a7f8f9d03683629ada8def3130c0ad4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:10:48.800954Z","signature_b64":"vP9aPVQe2rBFhs+gLmat0u7RCjRCXEdBs58LloxV0z/RSqqp3nVeC3aWLBagH6ZQEAT659/ITVLpyCwB/wSEDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f9a36edaadf56fa136366b755ce06656a7f8f9d03683629ada8def3130c0ad4","last_reissued_at":"2026-07-05T10:10:48.800433Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:10:48.800433Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.00854","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:10:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CijQyQhcekxHoLi1gTeIRAkxzKHjQAqtkc47458dgp0SpJxELKkX9ybZEOce0ww4SzG3tNZoK1cynWIbkOpIAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T21:00:00.188454Z"},"content_sha256":"37845ac98440908d793cc84350d6fabbaac2ff645be0ad0f5e00fbdd1d0465cc","schema_version":"1.0","event_id":"sha256:37845ac98440908d793cc84350d6fabbaac2ff645be0ad0f5e00fbdd1d0465cc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:R6NDN3NK35LPUE3DM23VLTQGMV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"math.OC","authors_text":"Nathalie Bartoli, Paul Saves, R\\'emy Priem, Sylvain Dubreuil, Youssef Diouane","submitted_at":"2025-02-02T16:57:05Z","abstract_excerpt":"Bayesian optimization (BO) is one of the most powerful strategies to solve computationally expensive-to-evaluate blackbox optimization problems. However, BO methods are conventionally used for optimization problems of small dimension because of the curse of dimensionality. In this paper, a high-dimensionnal optimization method incorporating linear embedding subspaces of small dimension is proposed to efficiently perform the optimization. An adaptive learning strategy for these linear embeddings is carried out in conjunction with the optimization. The resulting BO method, named efficient global"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.00854","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/2502.00854/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:10:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XbbU893PzavUlysMtWbxV2PU1XKqsjWZXSyCFro0Uj5lTjEEvUSunCzMH77+ecvDWOSKPqaLj//GVfDIyeKVAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T21:00:00.189213Z"},"content_sha256":"b6919750ff7f2f9236e8d19fdeb65fbbd42b15537343b69d1cd257fbb51947ca","schema_version":"1.0","event_id":"sha256:b6919750ff7f2f9236e8d19fdeb65fbbd42b15537343b69d1cd257fbb51947ca"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/R6NDN3NK35LPUE3DM23VLTQGMV/bundle.json","state_url":"https://pith.science/pith/R6NDN3NK35LPUE3DM23VLTQGMV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/R6NDN3NK35LPUE3DM23VLTQGMV/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-13T21:00:00Z","links":{"resolver":"https://pith.science/pith/R6NDN3NK35LPUE3DM23VLTQGMV","bundle":"https://pith.science/pith/R6NDN3NK35LPUE3DM23VLTQGMV/bundle.json","state":"https://pith.science/pith/R6NDN3NK35LPUE3DM23VLTQGMV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/R6NDN3NK35LPUE3DM23VLTQGMV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:R6NDN3NK35LPUE3DM23VLTQGMV","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0b77e94af603e19ea3b31882663416a1dacf96d2b347fde2d4a78288d9da4b0e","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"math.OC","submitted_at":"2025-02-02T16:57:05Z","title_canon_sha256":"2e1a90b1937f180443148457eaf44cf93e4849f6e670dde97daf7402154ef2a7"},"schema_version":"1.0","source":{"id":"2502.00854","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.00854","created_at":"2026-07-05T10:10:48Z"},{"alias_kind":"arxiv_version","alias_value":"2502.00854v1","created_at":"2026-07-05T10:10:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.00854","created_at":"2026-07-05T10:10:48Z"},{"alias_kind":"pith_short_12","alias_value":"R6NDN3NK35LP","created_at":"2026-07-05T10:10:48Z"},{"alias_kind":"pith_short_16","alias_value":"R6NDN3NK35LPUE3D","created_at":"2026-07-05T10:10:48Z"},{"alias_kind":"pith_short_8","alias_value":"R6NDN3NK","created_at":"2026-07-05T10:10:48Z"}],"graph_snapshots":[{"event_id":"sha256:b6919750ff7f2f9236e8d19fdeb65fbbd42b15537343b69d1cd257fbb51947ca","target":"graph","created_at":"2026-07-05T10:10:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2502.00854/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Bayesian optimization (BO) is one of the most powerful strategies to solve computationally expensive-to-evaluate blackbox optimization problems. However, BO methods are conventionally used for optimization problems of small dimension because of the curse of dimensionality. In this paper, a high-dimensionnal optimization method incorporating linear embedding subspaces of small dimension is proposed to efficiently perform the optimization. An adaptive learning strategy for these linear embeddings is carried out in conjunction with the optimization. The resulting BO method, named efficient global","authors_text":"Nathalie Bartoli, Paul Saves, R\\'emy Priem, Sylvain Dubreuil, Youssef Diouane","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"math.OC","submitted_at":"2025-02-02T16:57:05Z","title":"High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.00854","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:37845ac98440908d793cc84350d6fabbaac2ff645be0ad0f5e00fbdd1d0465cc","target":"record","created_at":"2026-07-05T10:10:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0b77e94af603e19ea3b31882663416a1dacf96d2b347fde2d4a78288d9da4b0e","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"math.OC","submitted_at":"2025-02-02T16:57:05Z","title_canon_sha256":"2e1a90b1937f180443148457eaf44cf93e4849f6e670dde97daf7402154ef2a7"},"schema_version":"1.0","source":{"id":"2502.00854","kind":"arxiv","version":1}},"canonical_sha256":"8f9a36edaadf56fa136366b755ce06656a7f8f9d03683629ada8def3130c0ad4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8f9a36edaadf56fa136366b755ce06656a7f8f9d03683629ada8def3130c0ad4","first_computed_at":"2026-07-05T10:10:48.800433Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:10:48.800433Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vP9aPVQe2rBFhs+gLmat0u7RCjRCXEdBs58LloxV0z/RSqqp3nVeC3aWLBagH6ZQEAT659/ITVLpyCwB/wSEDg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:10:48.800954Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.00854","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:37845ac98440908d793cc84350d6fabbaac2ff645be0ad0f5e00fbdd1d0465cc","sha256:b6919750ff7f2f9236e8d19fdeb65fbbd42b15537343b69d1cd257fbb51947ca"],"state_sha256":"81898b0e21fef7e1bd60cab1976e63bc6de121e56fd13a63efd152ba7d2d6623"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iV4fqR8Tn0c9AWtZ37A8KLpnxMKOibTi1IJzRzi7Ce3sx24AjSUUcNRFSQ/zfhOsLfSO84hc6I5mB+lzVbpwBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T21:00:00.202064Z","bundle_sha256":"67caa561d4c201a4b81b2b67adc61941c714ba9bb83906a189f1a8a24e833112"}}