{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:Z6K5RI2EZOUXHGNSIUUKTAT2SH","short_pith_number":"pith:Z6K5RI2E","canonical_record":{"source":{"id":"1902.09682","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-02-26T00:59:14Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"dead8e3e4ad8b9a30100d469a80f0e2fe2d91bb66f5135b5aa16f589baaf8c1c","abstract_canon_sha256":"aae3639f68eb1e9e2dd668fc7c82cc34ea475f3447df9fffc7c1fab068f3647f"},"schema_version":"1.0"},"canonical_sha256":"cf95d8a344cba97399b24528a9827a91e8f7adeded2817b211c34537188e943d","source":{"kind":"arxiv","id":"1902.09682","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.09682","created_at":"2026-05-17T23:52:35Z"},{"alias_kind":"arxiv_version","alias_value":"1902.09682v1","created_at":"2026-05-17T23:52:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.09682","created_at":"2026-05-17T23:52:35Z"},{"alias_kind":"pith_short_12","alias_value":"Z6K5RI2EZOUX","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"Z6K5RI2EZOUXHGNS","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"Z6K5RI2E","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:Z6K5RI2EZOUXHGNSIUUKTAT2SH","target":"record","payload":{"canonical_record":{"source":{"id":"1902.09682","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-02-26T00:59:14Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"dead8e3e4ad8b9a30100d469a80f0e2fe2d91bb66f5135b5aa16f589baaf8c1c","abstract_canon_sha256":"aae3639f68eb1e9e2dd668fc7c82cc34ea475f3447df9fffc7c1fab068f3647f"},"schema_version":"1.0"},"canonical_sha256":"cf95d8a344cba97399b24528a9827a91e8f7adeded2817b211c34537188e943d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:35.599011Z","signature_b64":"ok6SQ4bAnkx3juVUSy/AO9laMLWRaTMtJ+ON4K7O1Y3/2KzEjldyf8NohEEQtVgTWiilF4rThBIoOHRc6OURBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cf95d8a344cba97399b24528a9827a91e8f7adeded2817b211c34537188e943d","last_reissued_at":"2026-05-17T23:52:35.598391Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:35.598391Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1902.09682","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":"2026-05-17T23:52:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PmdX86hY2CfoOIAI73HtkAUkzpw1vifryhnsEwLfmwYWi4xDkSBC7PvidS4z3szx7ldHWXxTtKYDGd9bhCQfBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T21:13:41.731711Z"},"content_sha256":"5c7a42a961790e9a899473d7736c583e1006586f6fd3e7cbd411ced587b7cec8","schema_version":"1.0","event_id":"sha256:5c7a42a961790e9a899473d7736c583e1006586f6fd3e7cbd411ced587b7cec8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:Z6K5RI2EZOUXHGNSIUUKTAT2SH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multiscale Gaussian Process Level Set Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Shubhanshu Shekhar, Tara Javidi","submitted_at":"2019-02-26T00:59:14Z","abstract_excerpt":"In this paper, the problem of estimating the level set of a black-box function from noisy and expensive evaluation queries is considered. A new algorithm for this problem in the Bayesian framework with a Gaussian Process (GP) prior is proposed. The proposed algorithm employs a hierarchical sequence of partitions to explore different regions of the search space at varying levels of detail depending upon their proximity to the level set boundary. It is shown that this approach results in the algorithm having a low complexity implementation whose computational cost is significantly smaller than t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.09682","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":""},"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":"2026-05-17T23:52:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ufWHp/J0lCbrXUY3Z5krp+AoOe7KQr/rCmsfjS9i+jVg+guvIHoWO51MdAcKu4SSSHPaBI3ONx7YGhzEGQC3DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T21:13:41.732361Z"},"content_sha256":"ddec5f70e1355d5a1d3dc995be3d95cf58adf249f316f36afb17f55a1abcea25","schema_version":"1.0","event_id":"sha256:ddec5f70e1355d5a1d3dc995be3d95cf58adf249f316f36afb17f55a1abcea25"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Z6K5RI2EZOUXHGNSIUUKTAT2SH/bundle.json","state_url":"https://pith.science/pith/Z6K5RI2EZOUXHGNSIUUKTAT2SH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Z6K5RI2EZOUXHGNSIUUKTAT2SH/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":"2026-05-31T21:13:41Z","links":{"resolver":"https://pith.science/pith/Z6K5RI2EZOUXHGNSIUUKTAT2SH","bundle":"https://pith.science/pith/Z6K5RI2EZOUXHGNSIUUKTAT2SH/bundle.json","state":"https://pith.science/pith/Z6K5RI2EZOUXHGNSIUUKTAT2SH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Z6K5RI2EZOUXHGNSIUUKTAT2SH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:Z6K5RI2EZOUXHGNSIUUKTAT2SH","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":"aae3639f68eb1e9e2dd668fc7c82cc34ea475f3447df9fffc7c1fab068f3647f","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-02-26T00:59:14Z","title_canon_sha256":"dead8e3e4ad8b9a30100d469a80f0e2fe2d91bb66f5135b5aa16f589baaf8c1c"},"schema_version":"1.0","source":{"id":"1902.09682","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.09682","created_at":"2026-05-17T23:52:35Z"},{"alias_kind":"arxiv_version","alias_value":"1902.09682v1","created_at":"2026-05-17T23:52:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.09682","created_at":"2026-05-17T23:52:35Z"},{"alias_kind":"pith_short_12","alias_value":"Z6K5RI2EZOUX","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"Z6K5RI2EZOUXHGNS","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"Z6K5RI2E","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:ddec5f70e1355d5a1d3dc995be3d95cf58adf249f316f36afb17f55a1abcea25","target":"graph","created_at":"2026-05-17T23:52:35Z","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":"In this paper, the problem of estimating the level set of a black-box function from noisy and expensive evaluation queries is considered. A new algorithm for this problem in the Bayesian framework with a Gaussian Process (GP) prior is proposed. The proposed algorithm employs a hierarchical sequence of partitions to explore different regions of the search space at varying levels of detail depending upon their proximity to the level set boundary. It is shown that this approach results in the algorithm having a low complexity implementation whose computational cost is significantly smaller than t","authors_text":"Shubhanshu Shekhar, Tara Javidi","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-02-26T00:59:14Z","title":"Multiscale Gaussian Process Level Set Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.09682","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":"sha256:5c7a42a961790e9a899473d7736c583e1006586f6fd3e7cbd411ced587b7cec8","target":"record","created_at":"2026-05-17T23:52:35Z","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":"aae3639f68eb1e9e2dd668fc7c82cc34ea475f3447df9fffc7c1fab068f3647f","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-02-26T00:59:14Z","title_canon_sha256":"dead8e3e4ad8b9a30100d469a80f0e2fe2d91bb66f5135b5aa16f589baaf8c1c"},"schema_version":"1.0","source":{"id":"1902.09682","kind":"arxiv","version":1}},"canonical_sha256":"cf95d8a344cba97399b24528a9827a91e8f7adeded2817b211c34537188e943d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cf95d8a344cba97399b24528a9827a91e8f7adeded2817b211c34537188e943d","first_computed_at":"2026-05-17T23:52:35.598391Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:35.598391Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ok6SQ4bAnkx3juVUSy/AO9laMLWRaTMtJ+ON4K7O1Y3/2KzEjldyf8NohEEQtVgTWiilF4rThBIoOHRc6OURBw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:35.599011Z","signed_message":"canonical_sha256_bytes"},"source_id":"1902.09682","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5c7a42a961790e9a899473d7736c583e1006586f6fd3e7cbd411ced587b7cec8","sha256:ddec5f70e1355d5a1d3dc995be3d95cf58adf249f316f36afb17f55a1abcea25"],"state_sha256":"68824ab6e77e290102e1a0c34746c0cc948903abccba153e5ee7aa698261f045"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZqV/uW8RouRzQwu3wtvSywd2LRLuItM5KsTsKi0YrwVdE6eLY11ZQbal4oG/CsePCPZte9ztoix6VjI3b5P3Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T21:13:41.735481Z","bundle_sha256":"8076ac7dc2231f4161acd653fffd292b57e17db780e4ec76699b866df789225d"}}