{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:OU3QDPKUAV3TKIOGJX34IYHAFI","short_pith_number":"pith:OU3QDPKU","canonical_record":{"source":{"id":"1411.3685","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2014-11-13T19:57:54Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"d14c379953703a91f26a3330f30913ced64ba65078cd8e47728b3e2b16ea0200","abstract_canon_sha256":"9571324404c0884fd509efaf6840cfcb5c5d51e0dd0b0e7a56c18f8a549aae25"},"schema_version":"1.0"},"canonical_sha256":"753701bd5405773521c64df7c460e02a18e2392069db7f5371a22925ed3d354b","source":{"kind":"arxiv","id":"1411.3685","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1411.3685","created_at":"2026-05-18T02:21:51Z"},{"alias_kind":"arxiv_version","alias_value":"1411.3685v3","created_at":"2026-05-18T02:21:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1411.3685","created_at":"2026-05-18T02:21:51Z"},{"alias_kind":"pith_short_12","alias_value":"OU3QDPKUAV3T","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_16","alias_value":"OU3QDPKUAV3TKIOG","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_8","alias_value":"OU3QDPKU","created_at":"2026-05-18T12:28:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:OU3QDPKUAV3TKIOGJX34IYHAFI","target":"record","payload":{"canonical_record":{"source":{"id":"1411.3685","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2014-11-13T19:57:54Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"d14c379953703a91f26a3330f30913ced64ba65078cd8e47728b3e2b16ea0200","abstract_canon_sha256":"9571324404c0884fd509efaf6840cfcb5c5d51e0dd0b0e7a56c18f8a549aae25"},"schema_version":"1.0"},"canonical_sha256":"753701bd5405773521c64df7c460e02a18e2392069db7f5371a22925ed3d354b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:21:51.586119Z","signature_b64":"D3WLtQngPvZ3Il67zsy2dcZKDue18ti2L+2b24pks0JE4VwkjPiSpwPt7Cr9N8oK02JuyP3QMrq388jzycASDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"753701bd5405773521c64df7c460e02a18e2392069db7f5371a22925ed3d354b","last_reissued_at":"2026-05-18T02:21:51.585421Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:21:51.585421Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1411.3685","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:21:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hADvWSLZw8WJCw/xrdQXik+VRupymkJCuAA4JeFvgUmz4vZjj6k8ay9pvN06R/KifGubmaO8oqVyW5JCEW49BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T03:59:45.238140Z"},"content_sha256":"a775b5bdf0a6768dd63db4e3ff5c1ec16888f2fb28a94d0cad05b1b6b10d4a8e","schema_version":"1.0","event_id":"sha256:a775b5bdf0a6768dd63db4e3ff5c1ec16888f2fb28a94d0cad05b1b6b10d4a8e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:OU3QDPKUAV3TKIOGJX34IYHAFI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A warped kernel improving robustness in Bayesian optimization via random embeddings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"math.OC","authors_text":"David Ginsbourger ((M\\'ethodes d'Analyse Stochastique des Codes et Traitements Num\\'eriques), DEMO-ENSMSE), IMSV), Micka\\\"el Binois (DEMO-ENSMSE), Olivier Roustant ((M\\'ethodes d'Analyse Stochastique des Codes et Traitements Num\\'eriques)","submitted_at":"2014-11-13T19:57:54Z","abstract_excerpt":"This works extends the Random Embedding Bayesian Optimization approach by integrating a warping of the high dimensional subspace within the covariance kernel. The proposed warping, that relies on elementary geometric considerations, allows mitigating the drawbacks of the high extrinsic dimensionality while avoiding the algorithm to evaluate points giving redundant information. It also alleviates constraints on bound selection for the embedded domain, thus improving the robustness, as illustrated with a test case with 25 variables and intrinsic dimension 6."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1411.3685","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":""},"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":"2026-05-18T02:21:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"woG/+lKx8hFhCRVEKNJRA2BWXJZeGGOLCmguHebIOysrUdYzjIOBQ+hBRFctkc6C+oBQWsXwCq6SpYMCXCzBDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T03:59:45.238507Z"},"content_sha256":"5462203f6e4dfb4408fa8f2f2e0a57d25db2681854e8cbe065df5c72a205b8f0","schema_version":"1.0","event_id":"sha256:5462203f6e4dfb4408fa8f2f2e0a57d25db2681854e8cbe065df5c72a205b8f0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OU3QDPKUAV3TKIOGJX34IYHAFI/bundle.json","state_url":"https://pith.science/pith/OU3QDPKUAV3TKIOGJX34IYHAFI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OU3QDPKUAV3TKIOGJX34IYHAFI/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":"2026-06-07T03:59:45Z","links":{"resolver":"https://pith.science/pith/OU3QDPKUAV3TKIOGJX34IYHAFI","bundle":"https://pith.science/pith/OU3QDPKUAV3TKIOGJX34IYHAFI/bundle.json","state":"https://pith.science/pith/OU3QDPKUAV3TKIOGJX34IYHAFI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OU3QDPKUAV3TKIOGJX34IYHAFI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:OU3QDPKUAV3TKIOGJX34IYHAFI","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":"9571324404c0884fd509efaf6840cfcb5c5d51e0dd0b0e7a56c18f8a549aae25","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2014-11-13T19:57:54Z","title_canon_sha256":"d14c379953703a91f26a3330f30913ced64ba65078cd8e47728b3e2b16ea0200"},"schema_version":"1.0","source":{"id":"1411.3685","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1411.3685","created_at":"2026-05-18T02:21:51Z"},{"alias_kind":"arxiv_version","alias_value":"1411.3685v3","created_at":"2026-05-18T02:21:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1411.3685","created_at":"2026-05-18T02:21:51Z"},{"alias_kind":"pith_short_12","alias_value":"OU3QDPKUAV3T","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_16","alias_value":"OU3QDPKUAV3TKIOG","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_8","alias_value":"OU3QDPKU","created_at":"2026-05-18T12:28:43Z"}],"graph_snapshots":[{"event_id":"sha256:5462203f6e4dfb4408fa8f2f2e0a57d25db2681854e8cbe065df5c72a205b8f0","target":"graph","created_at":"2026-05-18T02:21:51Z","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"},"paper":{"abstract_excerpt":"This works extends the Random Embedding Bayesian Optimization approach by integrating a warping of the high dimensional subspace within the covariance kernel. The proposed warping, that relies on elementary geometric considerations, allows mitigating the drawbacks of the high extrinsic dimensionality while avoiding the algorithm to evaluate points giving redundant information. It also alleviates constraints on bound selection for the embedded domain, thus improving the robustness, as illustrated with a test case with 25 variables and intrinsic dimension 6.","authors_text":"David Ginsbourger ((M\\'ethodes d'Analyse Stochastique des Codes et Traitements Num\\'eriques), DEMO-ENSMSE), IMSV), Micka\\\"el Binois (DEMO-ENSMSE), Olivier Roustant ((M\\'ethodes d'Analyse Stochastique des Codes et Traitements Num\\'eriques)","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2014-11-13T19:57:54Z","title":"A warped kernel improving robustness in Bayesian optimization via random embeddings"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1411.3685","kind":"arxiv","version":3},"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:a775b5bdf0a6768dd63db4e3ff5c1ec16888f2fb28a94d0cad05b1b6b10d4a8e","target":"record","created_at":"2026-05-18T02:21:51Z","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":"9571324404c0884fd509efaf6840cfcb5c5d51e0dd0b0e7a56c18f8a549aae25","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2014-11-13T19:57:54Z","title_canon_sha256":"d14c379953703a91f26a3330f30913ced64ba65078cd8e47728b3e2b16ea0200"},"schema_version":"1.0","source":{"id":"1411.3685","kind":"arxiv","version":3}},"canonical_sha256":"753701bd5405773521c64df7c460e02a18e2392069db7f5371a22925ed3d354b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"753701bd5405773521c64df7c460e02a18e2392069db7f5371a22925ed3d354b","first_computed_at":"2026-05-18T02:21:51.585421Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:21:51.585421Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"D3WLtQngPvZ3Il67zsy2dcZKDue18ti2L+2b24pks0JE4VwkjPiSpwPt7Cr9N8oK02JuyP3QMrq388jzycASDw==","signature_status":"signed_v1","signed_at":"2026-05-18T02:21:51.586119Z","signed_message":"canonical_sha256_bytes"},"source_id":"1411.3685","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a775b5bdf0a6768dd63db4e3ff5c1ec16888f2fb28a94d0cad05b1b6b10d4a8e","sha256:5462203f6e4dfb4408fa8f2f2e0a57d25db2681854e8cbe065df5c72a205b8f0"],"state_sha256":"4fd62c1c270517fcaf5c2b304313aa5568942087e81c59f4deb888789ba55393"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CDK+Cdb5BR4HK+pTpoes/TjG5rSVgAk5B/U7fhaHaUXHQ4JVBG/axZnSWcan52i2Glv9cL59KbI3FIjjKAb8Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T03:59:45.240660Z","bundle_sha256":"cf91af361d582475a0e7fb8a988084bdd8f90d8258df648cc9d708e7a914c453"}}