{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:5SQZW6X7S2NZMTQPAJBMKEZJOP","short_pith_number":"pith:5SQZW6X7","canonical_record":{"source":{"id":"2307.13371","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-25T09:45:47Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"b001ee6294aa1919a16aa373a1f699d457848880cc29ea99d727876f668eeb26","abstract_canon_sha256":"d17ada8fa28dcb7ed2c64d78660c167582fa555ae053c79e8eb8e10d32eb55e0"},"schema_version":"1.0"},"canonical_sha256":"eca19b7aff969b964e0f0242c5132973efb931337755ca70bffaf9c3dd593676","source":{"kind":"arxiv","id":"2307.13371","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.13371","created_at":"2026-07-05T06:34:12Z"},{"alias_kind":"arxiv_version","alias_value":"2307.13371v1","created_at":"2026-07-05T06:34:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.13371","created_at":"2026-07-05T06:34:12Z"},{"alias_kind":"pith_short_12","alias_value":"5SQZW6X7S2NZ","created_at":"2026-07-05T06:34:12Z"},{"alias_kind":"pith_short_16","alias_value":"5SQZW6X7S2NZMTQP","created_at":"2026-07-05T06:34:12Z"},{"alias_kind":"pith_short_8","alias_value":"5SQZW6X7","created_at":"2026-07-05T06:34:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:5SQZW6X7S2NZMTQPAJBMKEZJOP","target":"record","payload":{"canonical_record":{"source":{"id":"2307.13371","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-25T09:45:47Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"b001ee6294aa1919a16aa373a1f699d457848880cc29ea99d727876f668eeb26","abstract_canon_sha256":"d17ada8fa28dcb7ed2c64d78660c167582fa555ae053c79e8eb8e10d32eb55e0"},"schema_version":"1.0"},"canonical_sha256":"eca19b7aff969b964e0f0242c5132973efb931337755ca70bffaf9c3dd593676","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:34:12.999242Z","signature_b64":"LgvVem9Yyqg8EoDpyjDc3xWAneP9Gu9Odn0VX3VknQEmFeFFZ9OncgPCpKEZAcBKcGh8Csoefg4GEbwgMckvDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eca19b7aff969b964e0f0242c5132973efb931337755ca70bffaf9c3dd593676","last_reissued_at":"2026-07-05T06:34:12.998842Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:34:12.998842Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2307.13371","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:34:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+69bpnujBvqdpbl1Qx/0CjAKNpz++cHLBDEjTKqzB/wYvsE8NbUv473W6YSajhlMg0JFXL+AVsszdI70C21yCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T23:51:54.855071Z"},"content_sha256":"70c4c42a2a294814950ef1a720ce78e27d08be2da6267f83c8b14becb7f9a797","schema_version":"1.0","event_id":"sha256:70c4c42a2a294814950ef1a720ce78e27d08be2da6267f83c8b14becb7f9a797"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:5SQZW6X7S2NZMTQPAJBMKEZJOP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexander Ladd, Fengxue Zhang, James Bowden, Jialin Song, Thomas A. Desautels, Yisong Yue, Yuxin Chen","submitted_at":"2023-07-25T09:45:47Z","abstract_excerpt":"We study Bayesian optimization (BO) in high-dimensional and non-stationary scenarios. Existing algorithms for such scenarios typically require extensive hyperparameter tuning, which limits their practical effectiveness. We propose a framework, called BALLET, which adaptively filters for a high-confidence region of interest (ROI) as a superlevel-set of a nonparametric probabilistic model such as a Gaussian process (GP). Our approach is easy to tune, and is able to focus on local region of the optimization space that can be tackled by existing BO methods. The key idea is to use two probabilistic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.13371","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/2307.13371/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:34:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U50hNsSY6Hz8qlGOGMfbylOLyU0sC9X/cgEJjVeDVNrjwQIB3rAmU+2bRF8dvRhgm1i/WwJQNfekhAjzwfQaDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T23:51:54.855564Z"},"content_sha256":"ed130a6a65849c182814fce85a585856439ead50fd608cf030a69c482e35dea7","schema_version":"1.0","event_id":"sha256:ed130a6a65849c182814fce85a585856439ead50fd608cf030a69c482e35dea7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5SQZW6X7S2NZMTQPAJBMKEZJOP/bundle.json","state_url":"https://pith.science/pith/5SQZW6X7S2NZMTQPAJBMKEZJOP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5SQZW6X7S2NZMTQPAJBMKEZJOP/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":"2026-08-23T23:51:54Z","links":{"resolver":"https://pith.science/pith/5SQZW6X7S2NZMTQPAJBMKEZJOP","bundle":"https://pith.science/pith/5SQZW6X7S2NZMTQPAJBMKEZJOP/bundle.json","state":"https://pith.science/pith/5SQZW6X7S2NZMTQPAJBMKEZJOP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5SQZW6X7S2NZMTQPAJBMKEZJOP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:5SQZW6X7S2NZMTQPAJBMKEZJOP","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":"d17ada8fa28dcb7ed2c64d78660c167582fa555ae053c79e8eb8e10d32eb55e0","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-25T09:45:47Z","title_canon_sha256":"b001ee6294aa1919a16aa373a1f699d457848880cc29ea99d727876f668eeb26"},"schema_version":"1.0","source":{"id":"2307.13371","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.13371","created_at":"2026-07-05T06:34:12Z"},{"alias_kind":"arxiv_version","alias_value":"2307.13371v1","created_at":"2026-07-05T06:34:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.13371","created_at":"2026-07-05T06:34:12Z"},{"alias_kind":"pith_short_12","alias_value":"5SQZW6X7S2NZ","created_at":"2026-07-05T06:34:12Z"},{"alias_kind":"pith_short_16","alias_value":"5SQZW6X7S2NZMTQP","created_at":"2026-07-05T06:34:12Z"},{"alias_kind":"pith_short_8","alias_value":"5SQZW6X7","created_at":"2026-07-05T06:34:12Z"}],"graph_snapshots":[{"event_id":"sha256:ed130a6a65849c182814fce85a585856439ead50fd608cf030a69c482e35dea7","target":"graph","created_at":"2026-07-05T06:34:12Z","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/2307.13371/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We study Bayesian optimization (BO) in high-dimensional and non-stationary scenarios. Existing algorithms for such scenarios typically require extensive hyperparameter tuning, which limits their practical effectiveness. We propose a framework, called BALLET, which adaptively filters for a high-confidence region of interest (ROI) as a superlevel-set of a nonparametric probabilistic model such as a Gaussian process (GP). Our approach is easy to tune, and is able to focus on local region of the optimization space that can be tackled by existing BO methods. The key idea is to use two probabilistic","authors_text":"Alexander Ladd, Fengxue Zhang, James Bowden, Jialin Song, Thomas A. Desautels, Yisong Yue, Yuxin Chen","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-25T09:45:47Z","title":"Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.13371","kind":"arxiv","version":1},"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":"sha256:70c4c42a2a294814950ef1a720ce78e27d08be2da6267f83c8b14becb7f9a797","target":"record","created_at":"2026-07-05T06:34:12Z","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":"d17ada8fa28dcb7ed2c64d78660c167582fa555ae053c79e8eb8e10d32eb55e0","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-25T09:45:47Z","title_canon_sha256":"b001ee6294aa1919a16aa373a1f699d457848880cc29ea99d727876f668eeb26"},"schema_version":"1.0","source":{"id":"2307.13371","kind":"arxiv","version":1}},"canonical_sha256":"eca19b7aff969b964e0f0242c5132973efb931337755ca70bffaf9c3dd593676","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eca19b7aff969b964e0f0242c5132973efb931337755ca70bffaf9c3dd593676","first_computed_at":"2026-07-05T06:34:12.998842Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:34:12.998842Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LgvVem9Yyqg8EoDpyjDc3xWAneP9Gu9Odn0VX3VknQEmFeFFZ9OncgPCpKEZAcBKcGh8Csoefg4GEbwgMckvDw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:34:12.999242Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.13371","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:70c4c42a2a294814950ef1a720ce78e27d08be2da6267f83c8b14becb7f9a797","sha256:ed130a6a65849c182814fce85a585856439ead50fd608cf030a69c482e35dea7"],"state_sha256":"45eb5347406fb8aba9603db1a47b1df6ba810ddc8b36f3ad436821c21089c03d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QKyYuWYSVOLG4J7f1pEx6SWcShSCll3N/rNi+IowEtk6Sfl2OMUyEQtD7mnJPXzeUt+PxgX9RE1EtnZNtI2dCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T23:51:54.860321Z","bundle_sha256":"82c0d7579c6bb17ae18b77c6f23af71a0249a8df72a7dbfcd40638021ab22ff7"}}