{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:GRMGUX4K22H5AZKDCJ4TYS5GEK","short_pith_number":"pith:GRMGUX4K","schema_version":"1.0","canonical_sha256":"34586a5f8ad68fd0654312793c4ba6228725a0d6165a183b91a530a5ffa3bb3c","source":{"kind":"arxiv","id":"2606.02351","version":1},"attestation_state":"computed","paper":{"title":"Local Preferential Bayesian Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"David Stenger, Johanna Menn, Miriam Kober, Paul Brunzema, Sebastian Trimpe","submitted_at":"2026-06-01T15:00:27Z","abstract_excerpt":"Bayesian optimization (BO) is a popular and effective approach for tuning expensive, noisy experiments, but requires the formulation of an explicit objective function. Preferential BO (PBO) removes this requirement by learning from pairwise human feedback, yet existing methods struggle to efficiently optimize beyond low- and medium-dimensional problems due to their global search approaches. We address this limitation by developing a family of local PBO methods that transfer key ideas from high-dimensional BO to the preferential setting. In particular, we introduce local PBO methods which adapt"},"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":"2606.02351","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-01T15:00:27Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"76af55d8aed0a0ccb70aacb462d7187ea326a8e9e30a5d93d10d8811cdbe216f","abstract_canon_sha256":"1d20891bb61641bc97712b8a9f15a268c93b823d672fd2c92ef4c98ed94ac79a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T03:04:56.808249Z","signature_b64":"JYq58dFOcRaJgYNdtDs3VgzPybjrZYXPwuAHD/jLRuSSq2u5/JsoOI3sqcwuvxnVZGspnJcSW6p7IEe+Br3bCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"34586a5f8ad68fd0654312793c4ba6228725a0d6165a183b91a530a5ffa3bb3c","last_reissued_at":"2026-06-02T03:04:56.807838Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T03:04:56.807838Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Local Preferential Bayesian Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"David Stenger, Johanna Menn, Miriam Kober, Paul Brunzema, Sebastian Trimpe","submitted_at":"2026-06-01T15:00:27Z","abstract_excerpt":"Bayesian optimization (BO) is a popular and effective approach for tuning expensive, noisy experiments, but requires the formulation of an explicit objective function. Preferential BO (PBO) removes this requirement by learning from pairwise human feedback, yet existing methods struggle to efficiently optimize beyond low- and medium-dimensional problems due to their global search approaches. We address this limitation by developing a family of local PBO methods that transfer key ideas from high-dimensional BO to the preferential setting. In particular, we introduce local PBO methods which adapt"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.02351","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/2606.02351/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":"2606.02351","created_at":"2026-06-02T03:04:56.807902+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.02351v1","created_at":"2026-06-02T03:04:56.807902+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.02351","created_at":"2026-06-02T03:04:56.807902+00:00"},{"alias_kind":"pith_short_12","alias_value":"GRMGUX4K22H5","created_at":"2026-06-02T03:04:56.807902+00:00"},{"alias_kind":"pith_short_16","alias_value":"GRMGUX4K22H5AZKD","created_at":"2026-06-02T03:04:56.807902+00:00"},{"alias_kind":"pith_short_8","alias_value":"GRMGUX4K","created_at":"2026-06-02T03:04:56.807902+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/GRMGUX4K22H5AZKDCJ4TYS5GEK","json":"https://pith.science/pith/GRMGUX4K22H5AZKDCJ4TYS5GEK.json","graph_json":"https://pith.science/api/pith-number/GRMGUX4K22H5AZKDCJ4TYS5GEK/graph.json","events_json":"https://pith.science/api/pith-number/GRMGUX4K22H5AZKDCJ4TYS5GEK/events.json","paper":"https://pith.science/paper/GRMGUX4K"},"agent_actions":{"view_html":"https://pith.science/pith/GRMGUX4K22H5AZKDCJ4TYS5GEK","download_json":"https://pith.science/pith/GRMGUX4K22H5AZKDCJ4TYS5GEK.json","view_paper":"https://pith.science/paper/GRMGUX4K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.02351&json=true","fetch_graph":"https://pith.science/api/pith-number/GRMGUX4K22H5AZKDCJ4TYS5GEK/graph.json","fetch_events":"https://pith.science/api/pith-number/GRMGUX4K22H5AZKDCJ4TYS5GEK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GRMGUX4K22H5AZKDCJ4TYS5GEK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GRMGUX4K22H5AZKDCJ4TYS5GEK/action/storage_attestation","attest_author":"https://pith.science/pith/GRMGUX4K22H5AZKDCJ4TYS5GEK/action/author_attestation","sign_citation":"https://pith.science/pith/GRMGUX4K22H5AZKDCJ4TYS5GEK/action/citation_signature","submit_replication":"https://pith.science/pith/GRMGUX4K22H5AZKDCJ4TYS5GEK/action/replication_record"}},"created_at":"2026-06-02T03:04:56.807902+00:00","updated_at":"2026-06-02T03:04:56.807902+00:00"}