{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:4KMQ36D75CMZCUEYW7GWQVNEKJ","short_pith_number":"pith:4KMQ36D7","canonical_record":{"source":{"id":"1703.01973","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-06T17:01:19Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"81ef2cd6bf54a696eb8a084b1c6caee0b1ae8a0890bc54fd5e3d1430c723417b","abstract_canon_sha256":"4396d53afea52af7dc7ea7ef78f8bc2d3415866d13682433d022f89cb9dc0a86"},"schema_version":"1.0"},"canonical_sha256":"e2990df87fe899915098b7cd6855a4524d9141c38186406902926c631d662013","source":{"kind":"arxiv","id":"1703.01973","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.01973","created_at":"2026-05-18T00:09:00Z"},{"alias_kind":"arxiv_version","alias_value":"1703.01973v2","created_at":"2026-05-18T00:09:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.01973","created_at":"2026-05-18T00:09:00Z"},{"alias_kind":"pith_short_12","alias_value":"4KMQ36D75CMZ","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"4KMQ36D75CMZCUEY","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"4KMQ36D7","created_at":"2026-05-18T12:31:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:4KMQ36D75CMZCUEYW7GWQVNEKJ","target":"record","payload":{"canonical_record":{"source":{"id":"1703.01973","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-06T17:01:19Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"81ef2cd6bf54a696eb8a084b1c6caee0b1ae8a0890bc54fd5e3d1430c723417b","abstract_canon_sha256":"4396d53afea52af7dc7ea7ef78f8bc2d3415866d13682433d022f89cb9dc0a86"},"schema_version":"1.0"},"canonical_sha256":"e2990df87fe899915098b7cd6855a4524d9141c38186406902926c631d662013","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:00.717647Z","signature_b64":"u/iYqj10vdhujiHtJQOPhgOpWlo+u0T1jzxNNGDhl+V24XRcbJ8HlsBGAFeKtFjXO/hzK9DeP65ILMi4hH4DAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e2990df87fe899915098b7cd6855a4524d9141c38186406902926c631d662013","last_reissued_at":"2026-05-18T00:09:00.716930Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:00.716930Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1703.01973","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:09:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3SWNPP/eMwqOJoDtHML53RN08+Y8St8SRLusRFdzZthSkqBWdxR0OKlzJLI/4E8GtfrsgeCFP0Q5xseS2bu5Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T11:44:08.502803Z"},"content_sha256":"c6478591e3d8967d2e5c6c5203fd3f9f9fed036ab621a8ffe91dae0cae4b8bcb","schema_version":"1.0","event_id":"sha256:c6478591e3d8967d2e5c6c5203fd3f9f9fed036ab621a8ffe91dae0cae4b8bcb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:4KMQ36D75CMZCUEYW7GWQVNEKJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Batched High-dimensional Bayesian Optimization via Structural Kernel Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"stat.ML","authors_text":"Chengtao Li, Pushmeet Kohli, Stefanie Jegelka, Zi Wang","submitted_at":"2017-03-06T17:01:19Z","abstract_excerpt":"Optimization of high-dimensional black-box functions is an extremely challenging problem. While Bayesian optimization has emerged as a popular approach for optimizing black-box functions, its applicability has been limited to low-dimensional problems due to its computational and statistical challenges arising from high-dimensional settings. In this paper, we propose to tackle these challenges by (1) assuming a latent additive structure in the function and inferring it properly for more efficient and effective BO, and (2) performing multiple evaluations in parallel to reduce the number of itera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.01973","kind":"arxiv","version":2},"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":"2026-05-18T00:09:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5acJbmWnXyehOcbDpjD6cadOc2an4qI2I6/GbYkPH2HWF15pMO50UPQBWgxnGWfYPlqHl3NZwyx7HVGo419bBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T11:44:08.503165Z"},"content_sha256":"fd05e4ea313e897ff0cdee9f97d66d1a3a0b7ef6b0e48355911a5f2896cf1bab","schema_version":"1.0","event_id":"sha256:fd05e4ea313e897ff0cdee9f97d66d1a3a0b7ef6b0e48355911a5f2896cf1bab"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4KMQ36D75CMZCUEYW7GWQVNEKJ/bundle.json","state_url":"https://pith.science/pith/4KMQ36D75CMZCUEYW7GWQVNEKJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4KMQ36D75CMZCUEYW7GWQVNEKJ/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":"2026-06-05T11:44:08Z","links":{"resolver":"https://pith.science/pith/4KMQ36D75CMZCUEYW7GWQVNEKJ","bundle":"https://pith.science/pith/4KMQ36D75CMZCUEYW7GWQVNEKJ/bundle.json","state":"https://pith.science/pith/4KMQ36D75CMZCUEYW7GWQVNEKJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4KMQ36D75CMZCUEYW7GWQVNEKJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:4KMQ36D75CMZCUEYW7GWQVNEKJ","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":"4396d53afea52af7dc7ea7ef78f8bc2d3415866d13682433d022f89cb9dc0a86","cross_cats_sorted":["cs.LG","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-06T17:01:19Z","title_canon_sha256":"81ef2cd6bf54a696eb8a084b1c6caee0b1ae8a0890bc54fd5e3d1430c723417b"},"schema_version":"1.0","source":{"id":"1703.01973","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.01973","created_at":"2026-05-18T00:09:00Z"},{"alias_kind":"arxiv_version","alias_value":"1703.01973v2","created_at":"2026-05-18T00:09:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.01973","created_at":"2026-05-18T00:09:00Z"},{"alias_kind":"pith_short_12","alias_value":"4KMQ36D75CMZ","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"4KMQ36D75CMZCUEY","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"4KMQ36D7","created_at":"2026-05-18T12:31:00Z"}],"graph_snapshots":[{"event_id":"sha256:fd05e4ea313e897ff0cdee9f97d66d1a3a0b7ef6b0e48355911a5f2896cf1bab","target":"graph","created_at":"2026-05-18T00:09:00Z","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":"Optimization of high-dimensional black-box functions is an extremely challenging problem. While Bayesian optimization has emerged as a popular approach for optimizing black-box functions, its applicability has been limited to low-dimensional problems due to its computational and statistical challenges arising from high-dimensional settings. In this paper, we propose to tackle these challenges by (1) assuming a latent additive structure in the function and inferring it properly for more efficient and effective BO, and (2) performing multiple evaluations in parallel to reduce the number of itera","authors_text":"Chengtao Li, Pushmeet Kohli, Stefanie Jegelka, Zi Wang","cross_cats":["cs.LG","math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-06T17:01:19Z","title":"Batched High-dimensional Bayesian Optimization via Structural Kernel Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.01973","kind":"arxiv","version":2},"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:c6478591e3d8967d2e5c6c5203fd3f9f9fed036ab621a8ffe91dae0cae4b8bcb","target":"record","created_at":"2026-05-18T00:09:00Z","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":"4396d53afea52af7dc7ea7ef78f8bc2d3415866d13682433d022f89cb9dc0a86","cross_cats_sorted":["cs.LG","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-06T17:01:19Z","title_canon_sha256":"81ef2cd6bf54a696eb8a084b1c6caee0b1ae8a0890bc54fd5e3d1430c723417b"},"schema_version":"1.0","source":{"id":"1703.01973","kind":"arxiv","version":2}},"canonical_sha256":"e2990df87fe899915098b7cd6855a4524d9141c38186406902926c631d662013","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e2990df87fe899915098b7cd6855a4524d9141c38186406902926c631d662013","first_computed_at":"2026-05-18T00:09:00.716930Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:09:00.716930Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"u/iYqj10vdhujiHtJQOPhgOpWlo+u0T1jzxNNGDhl+V24XRcbJ8HlsBGAFeKtFjXO/hzK9DeP65ILMi4hH4DAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:09:00.717647Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.01973","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c6478591e3d8967d2e5c6c5203fd3f9f9fed036ab621a8ffe91dae0cae4b8bcb","sha256:fd05e4ea313e897ff0cdee9f97d66d1a3a0b7ef6b0e48355911a5f2896cf1bab"],"state_sha256":"e811bcdb886a2673d0168f15196c0cd278edf08684176f0d21b59f2ac25e5650"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MGVn6+EjbcSeSZH6c4B9eYi/IwXnyosjOGo7I0E8JV6V1jBDSpv4EVP3as1R1ocuRgyaKYpZrwXkKJyTas8RBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T11:44:08.505264Z","bundle_sha256":"a7c090b314b2e005d83f5cf78bf3321794a228deb822ed77d6e495aabea88664"}}