{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:PEAZUCN32XR5PML6TNDYS74KZ4","short_pith_number":"pith:PEAZUCN3","canonical_record":{"source":{"id":"1812.02497","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-06T12:38:22Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"bfc3633a9d1442835d5bcb4ce0701220a081cc9e1590d997972a02d806db9ba2","abstract_canon_sha256":"c54c2829cebb28ad88b07b385a0f3aa8cc0dabb0da4276956c6a0da5c0536586"},"schema_version":"1.0"},"canonical_sha256":"79019a09bbd5e3d7b17e9b47897f8acf27e55929c6361b1192b1baad90b8f2fb","source":{"kind":"arxiv","id":"1812.02497","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.02497","created_at":"2026-05-17T23:58:55Z"},{"alias_kind":"arxiv_version","alias_value":"1812.02497v1","created_at":"2026-05-17T23:58:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.02497","created_at":"2026-05-17T23:58:55Z"},{"alias_kind":"pith_short_12","alias_value":"PEAZUCN32XR5","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"PEAZUCN32XR5PML6","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"PEAZUCN3","created_at":"2026-05-18T12:32:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:PEAZUCN32XR5PML6TNDYS74KZ4","target":"record","payload":{"canonical_record":{"source":{"id":"1812.02497","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-06T12:38:22Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"bfc3633a9d1442835d5bcb4ce0701220a081cc9e1590d997972a02d806db9ba2","abstract_canon_sha256":"c54c2829cebb28ad88b07b385a0f3aa8cc0dabb0da4276956c6a0da5c0536586"},"schema_version":"1.0"},"canonical_sha256":"79019a09bbd5e3d7b17e9b47897f8acf27e55929c6361b1192b1baad90b8f2fb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:55.543203Z","signature_b64":"WZRDHBoEwx9zC8Uu0/7GCwcXMPn8hhY8tSPKBGh4LyPIbCJRe8xwhJ53wDPeWiy/igT/aMFv8ToLtNcoEG1vBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79019a09bbd5e3d7b17e9b47897f8acf27e55929c6361b1192b1baad90b8f2fb","last_reissued_at":"2026-05-17T23:58:55.542688Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:55.542688Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.02497","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":"2026-05-17T23:58:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cP1YmISUD6ARkQ/wBmZKc06QraJ0oT0gvk80g5a0dKnteJ8gE+sZrT0FQH+qNfnYbOcIqOneBygeNxh5vrr7AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T16:08:19.790985Z"},"content_sha256":"d2209bdf304732893e50717e310798943d72291e79e319a35a2558187b76bb69","schema_version":"1.0","event_id":"sha256:d2209bdf304732893e50717e310798943d72291e79e319a35a2558187b76bb69"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:PEAZUCN32XR5PML6TNDYS74KZ4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Active Learning Methods based on Statistical Leverage Scores","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Cem Orhan, Oznur Tastan","submitted_at":"2018-12-06T12:38:22Z","abstract_excerpt":"In many real-world machine learning applications, unlabeled data are abundant whereas class labels are expensive and scarce. An active learner aims to obtain a model of high accuracy with as few labeled instances as possible by effectively selecting useful examples for labeling. We propose a new selection criterion that is based on statistical leverage scores and present two novel active learning methods based on this criterion: ALEVS for querying single example at each iteration and DBALEVS for querying a batch of examples. To assess the representativeness of the examples in the pool, ALEVS a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.02497","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":"2026-05-17T23:58:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2/hC24OSy2ep7aw4srPFGK2wrUccxZ0Ke4d0nxruVHXNf4sU366UNr0qHyVOVgGXEb6xPut+cYm8gYfeZHOKAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T16:08:19.791761Z"},"content_sha256":"d119916157df5d7da9ed45ec276115ac8e111c88761d9c8fbfc408597a386440","schema_version":"1.0","event_id":"sha256:d119916157df5d7da9ed45ec276115ac8e111c88761d9c8fbfc408597a386440"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PEAZUCN32XR5PML6TNDYS74KZ4/bundle.json","state_url":"https://pith.science/pith/PEAZUCN32XR5PML6TNDYS74KZ4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PEAZUCN32XR5PML6TNDYS74KZ4/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":"2026-08-06T16:08:19Z","links":{"resolver":"https://pith.science/pith/PEAZUCN32XR5PML6TNDYS74KZ4","bundle":"https://pith.science/pith/PEAZUCN32XR5PML6TNDYS74KZ4/bundle.json","state":"https://pith.science/pith/PEAZUCN32XR5PML6TNDYS74KZ4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PEAZUCN32XR5PML6TNDYS74KZ4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:PEAZUCN32XR5PML6TNDYS74KZ4","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":"c54c2829cebb28ad88b07b385a0f3aa8cc0dabb0da4276956c6a0da5c0536586","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-06T12:38:22Z","title_canon_sha256":"bfc3633a9d1442835d5bcb4ce0701220a081cc9e1590d997972a02d806db9ba2"},"schema_version":"1.0","source":{"id":"1812.02497","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.02497","created_at":"2026-05-17T23:58:55Z"},{"alias_kind":"arxiv_version","alias_value":"1812.02497v1","created_at":"2026-05-17T23:58:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.02497","created_at":"2026-05-17T23:58:55Z"},{"alias_kind":"pith_short_12","alias_value":"PEAZUCN32XR5","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"PEAZUCN32XR5PML6","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"PEAZUCN3","created_at":"2026-05-18T12:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:d119916157df5d7da9ed45ec276115ac8e111c88761d9c8fbfc408597a386440","target":"graph","created_at":"2026-05-17T23:58:55Z","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 many real-world machine learning applications, unlabeled data are abundant whereas class labels are expensive and scarce. An active learner aims to obtain a model of high accuracy with as few labeled instances as possible by effectively selecting useful examples for labeling. We propose a new selection criterion that is based on statistical leverage scores and present two novel active learning methods based on this criterion: ALEVS for querying single example at each iteration and DBALEVS for querying a batch of examples. To assess the representativeness of the examples in the pool, ALEVS a","authors_text":"Cem Orhan, Oznur Tastan","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-06T12:38:22Z","title":"Active Learning Methods based on Statistical Leverage Scores"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.02497","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:d2209bdf304732893e50717e310798943d72291e79e319a35a2558187b76bb69","target":"record","created_at":"2026-05-17T23:58:55Z","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":"c54c2829cebb28ad88b07b385a0f3aa8cc0dabb0da4276956c6a0da5c0536586","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-06T12:38:22Z","title_canon_sha256":"bfc3633a9d1442835d5bcb4ce0701220a081cc9e1590d997972a02d806db9ba2"},"schema_version":"1.0","source":{"id":"1812.02497","kind":"arxiv","version":1}},"canonical_sha256":"79019a09bbd5e3d7b17e9b47897f8acf27e55929c6361b1192b1baad90b8f2fb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"79019a09bbd5e3d7b17e9b47897f8acf27e55929c6361b1192b1baad90b8f2fb","first_computed_at":"2026-05-17T23:58:55.542688Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:58:55.542688Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WZRDHBoEwx9zC8Uu0/7GCwcXMPn8hhY8tSPKBGh4LyPIbCJRe8xwhJ53wDPeWiy/igT/aMFv8ToLtNcoEG1vBA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:58:55.543203Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.02497","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d2209bdf304732893e50717e310798943d72291e79e319a35a2558187b76bb69","sha256:d119916157df5d7da9ed45ec276115ac8e111c88761d9c8fbfc408597a386440"],"state_sha256":"0e81f0dece389e49554c5d995dcee4fe881d012442149896146f496175a11e95"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V2msOQ9GTF6nBIS3W2ION0USGBImdn6GxeN09h48VsJDcp1XzX4nmwaFmPKK1IC4hrQbhailDWS0AEXF2uwQAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T16:08:19.800262Z","bundle_sha256":"43427d1c8a01c3cacd85942537b7e9626195e7b03904dc4d78ba1a3118c5861f"}}