{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:IXES7JJENNG46S6BTS5L3H7TDH","short_pith_number":"pith:IXES7JJE","canonical_record":{"source":{"id":"1603.09620","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-03-31T15:00:06Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"785116e4d781f32ba9a930311df7ffa2a2918b98f2038efbbf859afccfca88b5","abstract_canon_sha256":"46d873c1df25bae5bc2348bfcac4957ed8b7a08715a1d3c9f5f8d59c98547485"},"schema_version":"1.0"},"canonical_sha256":"45c92fa5246b4dcf4bc19cbabd9ff319d69fa1878b97a394e37adea7810766e9","source":{"kind":"arxiv","id":"1603.09620","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.09620","created_at":"2026-05-18T01:03:40Z"},{"alias_kind":"arxiv_version","alias_value":"1603.09620v3","created_at":"2026-05-18T01:03:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.09620","created_at":"2026-05-18T01:03:40Z"},{"alias_kind":"pith_short_12","alias_value":"IXES7JJENNG4","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_16","alias_value":"IXES7JJENNG46S6B","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_8","alias_value":"IXES7JJE","created_at":"2026-05-18T12:30:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:IXES7JJENNG46S6BTS5L3H7TDH","target":"record","payload":{"canonical_record":{"source":{"id":"1603.09620","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-03-31T15:00:06Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"785116e4d781f32ba9a930311df7ffa2a2918b98f2038efbbf859afccfca88b5","abstract_canon_sha256":"46d873c1df25bae5bc2348bfcac4957ed8b7a08715a1d3c9f5f8d59c98547485"},"schema_version":"1.0"},"canonical_sha256":"45c92fa5246b4dcf4bc19cbabd9ff319d69fa1878b97a394e37adea7810766e9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:03:40.725569Z","signature_b64":"byKKtTD2rTYPlFvQ5QnFLkiYa6cUvL5wREzk8UAUkIbfONas0DWBrC6MClnQ5kzFPVdpZY0BxQc/9H7feB33Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45c92fa5246b4dcf4bc19cbabd9ff319d69fa1878b97a394e37adea7810766e9","last_reissued_at":"2026-05-18T01:03:40.725189Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:03:40.725189Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1603.09620","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":"2026-05-18T01:03:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B+bATVjJNTLMUwbdqbjK3Bac0TBP6PO4uTuTLppVDJaunEVnBaR72mg/tak5w5F0Jy/ccBhzJIp58G9lj9VSBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T19:40:21.981217Z"},"content_sha256":"bb514e407d176fe47dfe62c36849f15400553a5b8155799582762e2b9c8cbc06","schema_version":"1.0","event_id":"sha256:bb514e407d176fe47dfe62c36849f15400553a5b8155799582762e2b9c8cbc06"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:IXES7JJENNG46S6BTS5L3H7TDH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Online Optimization with Costly and Noisy Measurements using Random Fourier Expansions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Hans R. G. W. Verstraete, Laurens Bliek, Michel Verhaegen, Sander Wahls","submitted_at":"2016-03-31T15:00:06Z","abstract_excerpt":"This paper analyzes DONE, an online optimization algorithm that iteratively minimizes an unknown function based on costly and noisy measurements. The algorithm maintains a surrogate of the unknown function in the form of a random Fourier expansion (RFE). The surrogate is updated whenever a new measurement is available, and then used to determine the next measurement point. The algorithm is comparable to Bayesian optimization algorithms, but its computational complexity per iteration does not depend on the number of measurements. We derive several theoretical results that provide insight on how"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.09620","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":"2026-05-18T01:03:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GLHJWxi/nXVWNuk0PGLoiMmHcwKt3bC+R97psK84VFCUfK45arkE5xNn3D5l/80CjQOfyWwGYNVWZPN8KWQ+Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T19:40:21.981569Z"},"content_sha256":"b7426483a8c98993301b081a1cdec353d8d0b733d9ee16b02afc4fafb97a9346","schema_version":"1.0","event_id":"sha256:b7426483a8c98993301b081a1cdec353d8d0b733d9ee16b02afc4fafb97a9346"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IXES7JJENNG46S6BTS5L3H7TDH/bundle.json","state_url":"https://pith.science/pith/IXES7JJENNG46S6BTS5L3H7TDH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IXES7JJENNG46S6BTS5L3H7TDH/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":"2026-06-01T19:40:21Z","links":{"resolver":"https://pith.science/pith/IXES7JJENNG46S6BTS5L3H7TDH","bundle":"https://pith.science/pith/IXES7JJENNG46S6BTS5L3H7TDH/bundle.json","state":"https://pith.science/pith/IXES7JJENNG46S6BTS5L3H7TDH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IXES7JJENNG46S6BTS5L3H7TDH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:IXES7JJENNG46S6BTS5L3H7TDH","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":"46d873c1df25bae5bc2348bfcac4957ed8b7a08715a1d3c9f5f8d59c98547485","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-03-31T15:00:06Z","title_canon_sha256":"785116e4d781f32ba9a930311df7ffa2a2918b98f2038efbbf859afccfca88b5"},"schema_version":"1.0","source":{"id":"1603.09620","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.09620","created_at":"2026-05-18T01:03:40Z"},{"alias_kind":"arxiv_version","alias_value":"1603.09620v3","created_at":"2026-05-18T01:03:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.09620","created_at":"2026-05-18T01:03:40Z"},{"alias_kind":"pith_short_12","alias_value":"IXES7JJENNG4","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_16","alias_value":"IXES7JJENNG46S6B","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_8","alias_value":"IXES7JJE","created_at":"2026-05-18T12:30:22Z"}],"graph_snapshots":[{"event_id":"sha256:b7426483a8c98993301b081a1cdec353d8d0b733d9ee16b02afc4fafb97a9346","target":"graph","created_at":"2026-05-18T01:03:40Z","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 paper analyzes DONE, an online optimization algorithm that iteratively minimizes an unknown function based on costly and noisy measurements. The algorithm maintains a surrogate of the unknown function in the form of a random Fourier expansion (RFE). The surrogate is updated whenever a new measurement is available, and then used to determine the next measurement point. The algorithm is comparable to Bayesian optimization algorithms, but its computational complexity per iteration does not depend on the number of measurements. We derive several theoretical results that provide insight on how","authors_text":"Hans R. G. W. Verstraete, Laurens Bliek, Michel Verhaegen, Sander Wahls","cross_cats":["math.OC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-03-31T15:00:06Z","title":"Online Optimization with Costly and Noisy Measurements using Random Fourier Expansions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.09620","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:bb514e407d176fe47dfe62c36849f15400553a5b8155799582762e2b9c8cbc06","target":"record","created_at":"2026-05-18T01:03:40Z","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":"46d873c1df25bae5bc2348bfcac4957ed8b7a08715a1d3c9f5f8d59c98547485","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-03-31T15:00:06Z","title_canon_sha256":"785116e4d781f32ba9a930311df7ffa2a2918b98f2038efbbf859afccfca88b5"},"schema_version":"1.0","source":{"id":"1603.09620","kind":"arxiv","version":3}},"canonical_sha256":"45c92fa5246b4dcf4bc19cbabd9ff319d69fa1878b97a394e37adea7810766e9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"45c92fa5246b4dcf4bc19cbabd9ff319d69fa1878b97a394e37adea7810766e9","first_computed_at":"2026-05-18T01:03:40.725189Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:03:40.725189Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"byKKtTD2rTYPlFvQ5QnFLkiYa6cUvL5wREzk8UAUkIbfONas0DWBrC6MClnQ5kzFPVdpZY0BxQc/9H7feB33Cg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:03:40.725569Z","signed_message":"canonical_sha256_bytes"},"source_id":"1603.09620","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bb514e407d176fe47dfe62c36849f15400553a5b8155799582762e2b9c8cbc06","sha256:b7426483a8c98993301b081a1cdec353d8d0b733d9ee16b02afc4fafb97a9346"],"state_sha256":"33e7ed14480a4fa18216ad20a1b603d710f96039fc839cd40212a10b14980ae1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2YcwmQN48I/QMRtsHgSKEo3rYd32j91+HT+V01R7RKCeUEs8MqgW7N+t3Fu3a50yFgEV1nq8NsiZSzT26V/XAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T19:40:21.983416Z","bundle_sha256":"85485ad57dc08fb188b199a3bea4a280e62adfdba439a42662577d328c47cb91"}}