{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:SQ3H7SJAXICQ4RSLTGRRLOCDZD","short_pith_number":"pith:SQ3H7SJA","canonical_record":{"source":{"id":"1703.08816","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-26T13:29:25Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a90e051cc5fb735c1bde299a0557a93e72f5aa24b6f4d3f4ba39b5002eb64360","abstract_canon_sha256":"27f84feed47fa502ef9287065d0c9af79c075f0ba15e1335ae005ee25c9aa67d"},"schema_version":"1.0"},"canonical_sha256":"94367fc920ba050e464b99a315b843c8c21973f1c12e85d9b545460e58780f8c","source":{"kind":"arxiv","id":"1703.08816","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.08816","created_at":"2026-05-18T00:24:02Z"},{"alias_kind":"arxiv_version","alias_value":"1703.08816v2","created_at":"2026-05-18T00:24:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.08816","created_at":"2026-05-18T00:24:02Z"},{"alias_kind":"pith_short_12","alias_value":"SQ3H7SJAXICQ","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"SQ3H7SJAXICQ4RSL","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"SQ3H7SJA","created_at":"2026-05-18T12:31:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:SQ3H7SJAXICQ4RSLTGRRLOCDZD","target":"record","payload":{"canonical_record":{"source":{"id":"1703.08816","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-26T13:29:25Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a90e051cc5fb735c1bde299a0557a93e72f5aa24b6f4d3f4ba39b5002eb64360","abstract_canon_sha256":"27f84feed47fa502ef9287065d0c9af79c075f0ba15e1335ae005ee25c9aa67d"},"schema_version":"1.0"},"canonical_sha256":"94367fc920ba050e464b99a315b843c8c21973f1c12e85d9b545460e58780f8c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:02.416162Z","signature_b64":"0ZV9W6q316SwzpugjyB6YTQfpzFdqQG2Jq2fPytPG2bpcF+WAVYRgZ5F/zE5J1hKKvK1UJzT/do58T4cCvPpBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"94367fc920ba050e464b99a315b843c8c21973f1c12e85d9b545460e58780f8c","last_reissued_at":"2026-05-18T00:24:02.415591Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:02.415591Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1703.08816","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:24:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HQJ6d5Ow5B9GzlzCStBBvDflYdb9FfGD/3jo67q8FibpN2RJ0XVnK1GDHtpW65ugcD4gJCaxEwHjx1eYVGv9Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T04:30:02.894841Z"},"content_sha256":"9cc29998b333a82130724aae5f422f578b7a51eebdc32dd21af412727c170c0c","schema_version":"1.0","event_id":"sha256:9cc29998b333a82130724aae5f422f578b7a51eebdc32dd21af412727c170c0c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:SQ3H7SJAXICQ4RSLTGRRLOCDZD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Uncertainty quantification in graph-based classification of high dimensional data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Andrea L. Bertozzi, Andrew M. Stuart, Konstantinos C. Zygalakis, Xiyang Luo","submitted_at":"2017-03-26T13:29:25Z","abstract_excerpt":"Classification of high dimensional data finds wide-ranging applications. In many of these applications equipping the resulting classification with a measure of uncertainty may be as important as the classification itself. In this paper we introduce, develop algorithms for, and investigate the properties of, a variety of Bayesian models for the task of binary classification; via the posterior distribution on the classification labels, these methods automatically give measures of uncertainty. The methods are all based around the graph formulation of semi-supervised learning.\n  We provide a unifi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.08816","kind":"arxiv","version":2},"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":"2026-05-18T00:24:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZYU96f+DXNTflPU89G+WgtN+uIKu+5lFVmMOSXLuAe6ZgIEmuVkFuXk2zCJx8AHn6QUjR8bYYChqHTHpmI8LAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T04:30:02.895187Z"},"content_sha256":"148daf5cc25639b7da45c171d7989baaa7c5e1edbf327082b889da1f617dd69a","schema_version":"1.0","event_id":"sha256:148daf5cc25639b7da45c171d7989baaa7c5e1edbf327082b889da1f617dd69a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SQ3H7SJAXICQ4RSLTGRRLOCDZD/bundle.json","state_url":"https://pith.science/pith/SQ3H7SJAXICQ4RSLTGRRLOCDZD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SQ3H7SJAXICQ4RSLTGRRLOCDZD/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":"2026-05-28T04:30:02Z","links":{"resolver":"https://pith.science/pith/SQ3H7SJAXICQ4RSLTGRRLOCDZD","bundle":"https://pith.science/pith/SQ3H7SJAXICQ4RSLTGRRLOCDZD/bundle.json","state":"https://pith.science/pith/SQ3H7SJAXICQ4RSLTGRRLOCDZD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SQ3H7SJAXICQ4RSLTGRRLOCDZD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:SQ3H7SJAXICQ4RSLTGRRLOCDZD","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":"27f84feed47fa502ef9287065d0c9af79c075f0ba15e1335ae005ee25c9aa67d","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-26T13:29:25Z","title_canon_sha256":"a90e051cc5fb735c1bde299a0557a93e72f5aa24b6f4d3f4ba39b5002eb64360"},"schema_version":"1.0","source":{"id":"1703.08816","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.08816","created_at":"2026-05-18T00:24:02Z"},{"alias_kind":"arxiv_version","alias_value":"1703.08816v2","created_at":"2026-05-18T00:24:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.08816","created_at":"2026-05-18T00:24:02Z"},{"alias_kind":"pith_short_12","alias_value":"SQ3H7SJAXICQ","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"SQ3H7SJAXICQ4RSL","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"SQ3H7SJA","created_at":"2026-05-18T12:31:43Z"}],"graph_snapshots":[{"event_id":"sha256:148daf5cc25639b7da45c171d7989baaa7c5e1edbf327082b889da1f617dd69a","target":"graph","created_at":"2026-05-18T00:24:02Z","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":"Classification of high dimensional data finds wide-ranging applications. In many of these applications equipping the resulting classification with a measure of uncertainty may be as important as the classification itself. In this paper we introduce, develop algorithms for, and investigate the properties of, a variety of Bayesian models for the task of binary classification; via the posterior distribution on the classification labels, these methods automatically give measures of uncertainty. The methods are all based around the graph formulation of semi-supervised learning.\n  We provide a unifi","authors_text":"Andrea L. Bertozzi, Andrew M. Stuart, Konstantinos C. Zygalakis, Xiyang Luo","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-26T13:29:25Z","title":"Uncertainty quantification in graph-based classification of high dimensional data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.08816","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":"sha256:9cc29998b333a82130724aae5f422f578b7a51eebdc32dd21af412727c170c0c","target":"record","created_at":"2026-05-18T00:24:02Z","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":"27f84feed47fa502ef9287065d0c9af79c075f0ba15e1335ae005ee25c9aa67d","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-26T13:29:25Z","title_canon_sha256":"a90e051cc5fb735c1bde299a0557a93e72f5aa24b6f4d3f4ba39b5002eb64360"},"schema_version":"1.0","source":{"id":"1703.08816","kind":"arxiv","version":2}},"canonical_sha256":"94367fc920ba050e464b99a315b843c8c21973f1c12e85d9b545460e58780f8c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"94367fc920ba050e464b99a315b843c8c21973f1c12e85d9b545460e58780f8c","first_computed_at":"2026-05-18T00:24:02.415591Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:24:02.415591Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0ZV9W6q316SwzpugjyB6YTQfpzFdqQG2Jq2fPytPG2bpcF+WAVYRgZ5F/zE5J1hKKvK1UJzT/do58T4cCvPpBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:24:02.416162Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.08816","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9cc29998b333a82130724aae5f422f578b7a51eebdc32dd21af412727c170c0c","sha256:148daf5cc25639b7da45c171d7989baaa7c5e1edbf327082b889da1f617dd69a"],"state_sha256":"9576ec93fff1ec14ed468cbef6b8ced11de050c6495e8bc1fa3b9841192a12ae"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o05K3yJ4LikUxRtRzyX81ZPGJVHgHEJp9qP6h+H+aZa8pE1lpmPYGbfBIMY3fZk0+9B02nW9gffcjqHicoSwDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T04:30:02.897183Z","bundle_sha256":"ce37c0a9193385f01f6451e82e1497d2c6aa547fbd84de1bba90742547e9b2c5"}}