{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:C5ESI5RWL7E5DSN6LJJDMTIWQQ","short_pith_number":"pith:C5ESI5RW","schema_version":"1.0","canonical_sha256":"17492476365fc9d1c9be5a52364d16841c90f73ceb9aee36e0c8e9c6354360cd","source":{"kind":"arxiv","id":"2501.13697","version":1},"attestation_state":"computed","paper":{"title":"Safety in safe Bayesian optimization and its ramifications for control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","stat.ML"],"primary_cat":"eess.SY","authors_text":"Christian Fiedler, Johanna Menn, Sebastian Trimpe","submitted_at":"2025-01-23T14:24:11Z","abstract_excerpt":"A recurring and important task in control engineering is parameter tuning under constraints, which conceptually amounts to optimization of a blackbox function accessible only through noisy evaluations. For example, in control practice parameters of a pre-designed controller are often tuned online in feedback with a plant, and only safe parameter values should be tried, avoiding for example instability. Recently, machine learning methods have been deployed for this important problem, in particular, Bayesian optimization (BO). To handle safety constraints, algorithms from safe BO have been utili"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2501.13697","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2025-01-23T14:24:11Z","cross_cats_sorted":["cs.SY","stat.ML"],"title_canon_sha256":"42ed719b8f1b24dad4d7df8a691b155d1d3d282e7d2669003c668be8e5bfa7bd","abstract_canon_sha256":"f68740510eed0b920205b38c4e69fd4ebc714c3cd0ee965617e840c148315050"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:04:32.780587Z","signature_b64":"9OlHqbZhbHI/ySt0K7t6FmP7y9Iy0G/s8WD5rF+BlFH7AJq/Z8ctYP1JjdxZ+Ify/dwLhANuCUsWaeVLbDggBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"17492476365fc9d1c9be5a52364d16841c90f73ceb9aee36e0c8e9c6354360cd","last_reissued_at":"2026-07-05T10:04:32.780116Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:04:32.780116Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Safety in safe Bayesian optimization and its ramifications for control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","stat.ML"],"primary_cat":"eess.SY","authors_text":"Christian Fiedler, Johanna Menn, Sebastian Trimpe","submitted_at":"2025-01-23T14:24:11Z","abstract_excerpt":"A recurring and important task in control engineering is parameter tuning under constraints, which conceptually amounts to optimization of a blackbox function accessible only through noisy evaluations. For example, in control practice parameters of a pre-designed controller are often tuned online in feedback with a plant, and only safe parameter values should be tried, avoiding for example instability. Recently, machine learning methods have been deployed for this important problem, in particular, Bayesian optimization (BO). To handle safety constraints, algorithms from safe BO have been utili"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.13697","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/2501.13697/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2501.13697","created_at":"2026-07-05T10:04:32.780173+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.13697v1","created_at":"2026-07-05T10:04:32.780173+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.13697","created_at":"2026-07-05T10:04:32.780173+00:00"},{"alias_kind":"pith_short_12","alias_value":"C5ESI5RWL7E5","created_at":"2026-07-05T10:04:32.780173+00:00"},{"alias_kind":"pith_short_16","alias_value":"C5ESI5RWL7E5DSN6","created_at":"2026-07-05T10:04:32.780173+00:00"},{"alias_kind":"pith_short_8","alias_value":"C5ESI5RW","created_at":"2026-07-05T10:04:32.780173+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/C5ESI5RWL7E5DSN6LJJDMTIWQQ","json":"https://pith.science/pith/C5ESI5RWL7E5DSN6LJJDMTIWQQ.json","graph_json":"https://pith.science/api/pith-number/C5ESI5RWL7E5DSN6LJJDMTIWQQ/graph.json","events_json":"https://pith.science/api/pith-number/C5ESI5RWL7E5DSN6LJJDMTIWQQ/events.json","paper":"https://pith.science/paper/C5ESI5RW"},"agent_actions":{"view_html":"https://pith.science/pith/C5ESI5RWL7E5DSN6LJJDMTIWQQ","download_json":"https://pith.science/pith/C5ESI5RWL7E5DSN6LJJDMTIWQQ.json","view_paper":"https://pith.science/paper/C5ESI5RW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.13697&json=true","fetch_graph":"https://pith.science/api/pith-number/C5ESI5RWL7E5DSN6LJJDMTIWQQ/graph.json","fetch_events":"https://pith.science/api/pith-number/C5ESI5RWL7E5DSN6LJJDMTIWQQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C5ESI5RWL7E5DSN6LJJDMTIWQQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C5ESI5RWL7E5DSN6LJJDMTIWQQ/action/storage_attestation","attest_author":"https://pith.science/pith/C5ESI5RWL7E5DSN6LJJDMTIWQQ/action/author_attestation","sign_citation":"https://pith.science/pith/C5ESI5RWL7E5DSN6LJJDMTIWQQ/action/citation_signature","submit_replication":"https://pith.science/pith/C5ESI5RWL7E5DSN6LJJDMTIWQQ/action/replication_record"}},"created_at":"2026-07-05T10:04:32.780173+00:00","updated_at":"2026-07-05T10:04:32.780173+00:00"}