{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:G5NC7R6RUZYVLOZYXOXIDASRVC","short_pith_number":"pith:G5NC7R6R","canonical_record":{"source":{"id":"1901.03357","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-01-10T19:50:12Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"140423bda0e7cac7bfdf186f909769e3396087fac066b1f70b3b7eba02491aec","abstract_canon_sha256":"bc6c38e72e90997ff6195ccfbb7ff353ac6b29f833d18a9aaa04e2a0bd3fb27b"},"schema_version":"1.0"},"canonical_sha256":"375a2fc7d1a67155bb38bbae818251a8b122e4bcbd02d33ce8184f70acf6bc56","source":{"kind":"arxiv","id":"1901.03357","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.03357","created_at":"2026-05-17T23:49:39Z"},{"alias_kind":"arxiv_version","alias_value":"1901.03357v2","created_at":"2026-05-17T23:49:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.03357","created_at":"2026-05-17T23:49:39Z"},{"alias_kind":"pith_short_12","alias_value":"G5NC7R6RUZYV","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"G5NC7R6RUZYVLOZY","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"G5NC7R6R","created_at":"2026-05-18T12:33:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:G5NC7R6RUZYVLOZYXOXIDASRVC","target":"record","payload":{"canonical_record":{"source":{"id":"1901.03357","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-01-10T19:50:12Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"140423bda0e7cac7bfdf186f909769e3396087fac066b1f70b3b7eba02491aec","abstract_canon_sha256":"bc6c38e72e90997ff6195ccfbb7ff353ac6b29f833d18a9aaa04e2a0bd3fb27b"},"schema_version":"1.0"},"canonical_sha256":"375a2fc7d1a67155bb38bbae818251a8b122e4bcbd02d33ce8184f70acf6bc56","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:39.657488Z","signature_b64":"xtsRhIJN7eYO5xKLDntlk3Y0H37RO29MOkEMXKC7KRNsnpKRZ5rsb+zAv4tx9SHWscN+snxPjz357OOzOUAvCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"375a2fc7d1a67155bb38bbae818251a8b122e4bcbd02d33ce8184f70acf6bc56","last_reissued_at":"2026-05-17T23:49:39.656868Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:39.656868Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.03357","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":"2026-05-17T23:49:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dbjc9QWEJOqdWlm+z6H7stNxQGzQm7vnIxyhG3nGweF/JyRBzxkXoxNd/W0h+waP1eHXltKEFzQ5EPZMqPKiAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T06:44:44.589069Z"},"content_sha256":"6be254310e0e4f36cf248d6083d0ec4ed74bf38d00cffed5b48691c1f82cc068","schema_version":"1.0","event_id":"sha256:6be254310e0e4f36cf248d6083d0ec4ed74bf38d00cffed5b48691c1f82cc068"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:G5NC7R6RUZYVLOZYXOXIDASRVC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"No-Regret Bayesian Optimization with Unknown Hyperparameters","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Andreas Krause, Angela P. Schoellig, Felix Berkenkamp","submitted_at":"2019-01-10T19:50:12Z","abstract_excerpt":"Bayesian optimization (BO) based on Gaussian process models is a powerful paradigm to optimize black-box functions that are expensive to evaluate. While several BO algorithms provably converge to the global optimum of the unknown function, they assume that the hyperparameters of the kernel are known in advance. This is not the case in practice and misspecification often causes these algorithms to converge to poor local optima. In this paper, we present the first BO algorithm that is provably no-regret and converges to the optimum without knowledge of the hyperparameters. During optimization we"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.03357","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":"2026-05-17T23:49:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p2Rpfktqnq73a7CAW26yAl8dAykEWuF3mWmCrg9FfXgGQ3gIjaG9kS7/dCcK5SlR7Vy4ss4aPhJrr46+W8ctDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T06:44:44.589643Z"},"content_sha256":"cc8789403bab5b900f52a1bb9f246438e714465f2a76757100d470b256c19815","schema_version":"1.0","event_id":"sha256:cc8789403bab5b900f52a1bb9f246438e714465f2a76757100d470b256c19815"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/G5NC7R6RUZYVLOZYXOXIDASRVC/bundle.json","state_url":"https://pith.science/pith/G5NC7R6RUZYVLOZYXOXIDASRVC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/G5NC7R6RUZYVLOZYXOXIDASRVC/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":"2026-05-21T06:44:44Z","links":{"resolver":"https://pith.science/pith/G5NC7R6RUZYVLOZYXOXIDASRVC","bundle":"https://pith.science/pith/G5NC7R6RUZYVLOZYXOXIDASRVC/bundle.json","state":"https://pith.science/pith/G5NC7R6RUZYVLOZYXOXIDASRVC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/G5NC7R6RUZYVLOZYXOXIDASRVC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:G5NC7R6RUZYVLOZYXOXIDASRVC","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":"bc6c38e72e90997ff6195ccfbb7ff353ac6b29f833d18a9aaa04e2a0bd3fb27b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-01-10T19:50:12Z","title_canon_sha256":"140423bda0e7cac7bfdf186f909769e3396087fac066b1f70b3b7eba02491aec"},"schema_version":"1.0","source":{"id":"1901.03357","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.03357","created_at":"2026-05-17T23:49:39Z"},{"alias_kind":"arxiv_version","alias_value":"1901.03357v2","created_at":"2026-05-17T23:49:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.03357","created_at":"2026-05-17T23:49:39Z"},{"alias_kind":"pith_short_12","alias_value":"G5NC7R6RUZYV","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"G5NC7R6RUZYVLOZY","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"G5NC7R6R","created_at":"2026-05-18T12:33:18Z"}],"graph_snapshots":[{"event_id":"sha256:cc8789403bab5b900f52a1bb9f246438e714465f2a76757100d470b256c19815","target":"graph","created_at":"2026-05-17T23:49:39Z","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":"Bayesian optimization (BO) based on Gaussian process models is a powerful paradigm to optimize black-box functions that are expensive to evaluate. While several BO algorithms provably converge to the global optimum of the unknown function, they assume that the hyperparameters of the kernel are known in advance. This is not the case in practice and misspecification often causes these algorithms to converge to poor local optima. In this paper, we present the first BO algorithm that is provably no-regret and converges to the optimum without knowledge of the hyperparameters. During optimization we","authors_text":"Andreas Krause, Angela P. Schoellig, Felix Berkenkamp","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-01-10T19:50:12Z","title":"No-Regret Bayesian Optimization with Unknown Hyperparameters"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.03357","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":"sha256:6be254310e0e4f36cf248d6083d0ec4ed74bf38d00cffed5b48691c1f82cc068","target":"record","created_at":"2026-05-17T23:49:39Z","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":"bc6c38e72e90997ff6195ccfbb7ff353ac6b29f833d18a9aaa04e2a0bd3fb27b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-01-10T19:50:12Z","title_canon_sha256":"140423bda0e7cac7bfdf186f909769e3396087fac066b1f70b3b7eba02491aec"},"schema_version":"1.0","source":{"id":"1901.03357","kind":"arxiv","version":2}},"canonical_sha256":"375a2fc7d1a67155bb38bbae818251a8b122e4bcbd02d33ce8184f70acf6bc56","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"375a2fc7d1a67155bb38bbae818251a8b122e4bcbd02d33ce8184f70acf6bc56","first_computed_at":"2026-05-17T23:49:39.656868Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:49:39.656868Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xtsRhIJN7eYO5xKLDntlk3Y0H37RO29MOkEMXKC7KRNsnpKRZ5rsb+zAv4tx9SHWscN+snxPjz357OOzOUAvCg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:49:39.657488Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.03357","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6be254310e0e4f36cf248d6083d0ec4ed74bf38d00cffed5b48691c1f82cc068","sha256:cc8789403bab5b900f52a1bb9f246438e714465f2a76757100d470b256c19815"],"state_sha256":"1fb86ec36d5c8d738ac4c8419437c8277ec804784ca7782973101de1edc66429"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6cfUVk1VKLabnNURg+/idAUrQa4NNWAA0rU7GBGXEiEGWlBL+6pmjocMH8DFhEXWYGTsJMip2nHmJNUI8BvcDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T06:44:44.593120Z","bundle_sha256":"6803c9bb8b9a623058e72628944db271d852ac2b22de6ba10095c507885bf121"}}