{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:V3MHVE6CDUNQIXSNGYAQG3RZDQ","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":"94a9c40c02ae0e7476f85ca55cb5a9d797aa897dac2e3e7a53a23f5e5b208c4d","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-03-26T06:41:07Z","title_canon_sha256":"f8b63316febfc0001e0be67ba4b0e668b63d846c29e441c5abc5792d91155c9e"},"schema_version":"1.0","source":{"id":"2503.20272","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.20272","created_at":"2026-06-10T01:08:27Z"},{"alias_kind":"arxiv_version","alias_value":"2503.20272v2","created_at":"2026-06-10T01:08:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.20272","created_at":"2026-06-10T01:08:27Z"},{"alias_kind":"pith_short_12","alias_value":"V3MHVE6CDUNQ","created_at":"2026-06-10T01:08:27Z"},{"alias_kind":"pith_short_16","alias_value":"V3MHVE6CDUNQIXSN","created_at":"2026-06-10T01:08:27Z"},{"alias_kind":"pith_short_8","alias_value":"V3MHVE6C","created_at":"2026-06-10T01:08:27Z"}],"graph_snapshots":[{"event_id":"sha256:b3ee6dfc575262d2ee3e51335e5021134da4693623d54c23299a0df346583200","target":"graph","created_at":"2026-06-10T01:08:27Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2503.20272/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The level set estimation problem seeks to identify regions within a set of candidate points where an unknown and costly to evaluate function's value exceeds a specified threshold, providing an efficient alternative to exhaustive evaluations of function values. Traditional methods often use sequential optimization strategies to find $\\epsilon$-accurate solutions, which permit a margin around the threshold contour but frequently lack effective stopping criteria, leading to excessive exploration and inefficiencies. This paper introduces an acquisition strategy for level set estimation that incorp","authors_text":"Hideaki Ishibashi, Hideitsu Hino, Kentaro Kutsukake, Kota Matsui","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-03-26T06:41:07Z","title":"An $(\\epsilon,\\delta)$-accurate level set estimation with a stopping criterion"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.20272","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":"sha256:21f13fa5c0a5f7e02f5ed855e932a6d478bb85aff5f5e9bc20d19481154981a5","target":"record","created_at":"2026-06-10T01:08:27Z","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":"94a9c40c02ae0e7476f85ca55cb5a9d797aa897dac2e3e7a53a23f5e5b208c4d","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-03-26T06:41:07Z","title_canon_sha256":"f8b63316febfc0001e0be67ba4b0e668b63d846c29e441c5abc5792d91155c9e"},"schema_version":"1.0","source":{"id":"2503.20272","kind":"arxiv","version":2}},"canonical_sha256":"aed87a93c21d1b045e4d3601036e391c2013427118fd499ec7490c803a144d1e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aed87a93c21d1b045e4d3601036e391c2013427118fd499ec7490c803a144d1e","first_computed_at":"2026-06-10T01:08:27.455532Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-10T01:08:27.455532Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wtrxS4pG/Qk1aTknV3bhQYqcnF672LDnghf0CsJSKNn5ZC4aorFsp63XQTh/M3IO1Nlci618mlh3y+MzCwEuCg==","signature_status":"signed_v1","signed_at":"2026-06-10T01:08:27.456617Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.20272","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:21f13fa5c0a5f7e02f5ed855e932a6d478bb85aff5f5e9bc20d19481154981a5","sha256:b3ee6dfc575262d2ee3e51335e5021134da4693623d54c23299a0df346583200"],"state_sha256":"129e10b59df6efeb93792f091593b569b370a347fda2a1847236532765df4351"}