{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:OSRZZEWTV56Y5PXKKQWGZA3JS7","short_pith_number":"pith:OSRZZEWT","canonical_record":{"source":{"id":"1801.04701","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-01-15T08:56:49Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"4c8fc945475bbb0ea60d6c008b5938f6aa941e2533c35f40e3dd5137c9791056","abstract_canon_sha256":"d8a33dff342217fcfa083330f1dbb873ca995de1d5c9eac44c633ff577096ac7"},"schema_version":"1.0"},"canonical_sha256":"74a39c92d3af7d8ebeea542c6c836997ecb9df32c360c899b19860fbd7a06716","source":{"kind":"arxiv","id":"1801.04701","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.04701","created_at":"2026-05-18T00:26:03Z"},{"alias_kind":"arxiv_version","alias_value":"1801.04701v1","created_at":"2026-05-18T00:26:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.04701","created_at":"2026-05-18T00:26:03Z"},{"alias_kind":"pith_short_12","alias_value":"OSRZZEWTV56Y","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OSRZZEWTV56Y5PXK","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OSRZZEWT","created_at":"2026-05-18T12:32:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:OSRZZEWTV56Y5PXKKQWGZA3JS7","target":"record","payload":{"canonical_record":{"source":{"id":"1801.04701","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-01-15T08:56:49Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"4c8fc945475bbb0ea60d6c008b5938f6aa941e2533c35f40e3dd5137c9791056","abstract_canon_sha256":"d8a33dff342217fcfa083330f1dbb873ca995de1d5c9eac44c633ff577096ac7"},"schema_version":"1.0"},"canonical_sha256":"74a39c92d3af7d8ebeea542c6c836997ecb9df32c360c899b19860fbd7a06716","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:26:03.530802Z","signature_b64":"tR0WbSOoTiUihniUpQbfClY5H5ZZIbhuSSC391YLb9ZvG+x6OsBQlHbRJq3VMwCHBf/U4+70d6ZXAkIsrw46CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"74a39c92d3af7d8ebeea542c6c836997ecb9df32c360c899b19860fbd7a06716","last_reissued_at":"2026-05-18T00:26:03.530138Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:26:03.530138Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1801.04701","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":"2026-05-18T00:26:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4HOruDeCKN61Wqbgb/GSCVL2BiptYdRfLMHyle2zlM2/c8g0kAW7HQQeZoP3U4TxOGFKmyAG+TKAhFid4OJVBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T00:35:19.969998Z"},"content_sha256":"152db030f39208b783c4ea71dfa0df4f18dceb19c6254c4ddb49a70e106e691d","schema_version":"1.0","event_id":"sha256:152db030f39208b783c4ea71dfa0df4f18dceb19c6254c4ddb49a70e106e691d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:OSRZZEWTV56Y5PXKKQWGZA3JS7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"tau-FPL: Tolerance-Constrained Learning in Linear Time","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ao Zhang, Hongyuan Zha, Jian Pu, Junchi Yan, Jun Wang, Nan Li","submitted_at":"2018-01-15T08:56:49Z","abstract_excerpt":"Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which lack consistency in methodology because they do not strictly adhere to the false-positive rate constraint. In this paper, we propose a novel scoring-thresholding approach, tau-False Positive Learning (tau-FPL) to address this problem. We show the scoring problem which takes the false-positive rate tolerance into accounts can be efficiently so"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.04701","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":"2026-05-18T00:26:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nnechehVFbUDBbKIBx3GHCeRJDdSKu28skzbDDNE0pXL9CwE9Vqz6XWFtcjc9g8sGqSJRgAiNjr2Uamd5z1WDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T00:35:19.970377Z"},"content_sha256":"d2c09f42e5f883359e23dae27ea60e44a5246b8cf32c15dcfdbd45e93cbdebdf","schema_version":"1.0","event_id":"sha256:d2c09f42e5f883359e23dae27ea60e44a5246b8cf32c15dcfdbd45e93cbdebdf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OSRZZEWTV56Y5PXKKQWGZA3JS7/bundle.json","state_url":"https://pith.science/pith/OSRZZEWTV56Y5PXKKQWGZA3JS7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OSRZZEWTV56Y5PXKKQWGZA3JS7/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":"2026-05-23T00:35:19Z","links":{"resolver":"https://pith.science/pith/OSRZZEWTV56Y5PXKKQWGZA3JS7","bundle":"https://pith.science/pith/OSRZZEWTV56Y5PXKKQWGZA3JS7/bundle.json","state":"https://pith.science/pith/OSRZZEWTV56Y5PXKKQWGZA3JS7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OSRZZEWTV56Y5PXKKQWGZA3JS7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:OSRZZEWTV56Y5PXKKQWGZA3JS7","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":"d8a33dff342217fcfa083330f1dbb873ca995de1d5c9eac44c633ff577096ac7","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-01-15T08:56:49Z","title_canon_sha256":"4c8fc945475bbb0ea60d6c008b5938f6aa941e2533c35f40e3dd5137c9791056"},"schema_version":"1.0","source":{"id":"1801.04701","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.04701","created_at":"2026-05-18T00:26:03Z"},{"alias_kind":"arxiv_version","alias_value":"1801.04701v1","created_at":"2026-05-18T00:26:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.04701","created_at":"2026-05-18T00:26:03Z"},{"alias_kind":"pith_short_12","alias_value":"OSRZZEWTV56Y","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OSRZZEWTV56Y5PXK","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OSRZZEWT","created_at":"2026-05-18T12:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:d2c09f42e5f883359e23dae27ea60e44a5246b8cf32c15dcfdbd45e93cbdebdf","target":"graph","created_at":"2026-05-18T00:26:03Z","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":"Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which lack consistency in methodology because they do not strictly adhere to the false-positive rate constraint. In this paper, we propose a novel scoring-thresholding approach, tau-False Positive Learning (tau-FPL) to address this problem. We show the scoring problem which takes the false-positive rate tolerance into accounts can be efficiently so","authors_text":"Ao Zhang, Hongyuan Zha, Jian Pu, Junchi Yan, Jun Wang, Nan Li","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-01-15T08:56:49Z","title":"tau-FPL: Tolerance-Constrained Learning in Linear Time"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.04701","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":"sha256:152db030f39208b783c4ea71dfa0df4f18dceb19c6254c4ddb49a70e106e691d","target":"record","created_at":"2026-05-18T00:26:03Z","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":"d8a33dff342217fcfa083330f1dbb873ca995de1d5c9eac44c633ff577096ac7","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-01-15T08:56:49Z","title_canon_sha256":"4c8fc945475bbb0ea60d6c008b5938f6aa941e2533c35f40e3dd5137c9791056"},"schema_version":"1.0","source":{"id":"1801.04701","kind":"arxiv","version":1}},"canonical_sha256":"74a39c92d3af7d8ebeea542c6c836997ecb9df32c360c899b19860fbd7a06716","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"74a39c92d3af7d8ebeea542c6c836997ecb9df32c360c899b19860fbd7a06716","first_computed_at":"2026-05-18T00:26:03.530138Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:26:03.530138Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tR0WbSOoTiUihniUpQbfClY5H5ZZIbhuSSC391YLb9ZvG+x6OsBQlHbRJq3VMwCHBf/U4+70d6ZXAkIsrw46CA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:26:03.530802Z","signed_message":"canonical_sha256_bytes"},"source_id":"1801.04701","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:152db030f39208b783c4ea71dfa0df4f18dceb19c6254c4ddb49a70e106e691d","sha256:d2c09f42e5f883359e23dae27ea60e44a5246b8cf32c15dcfdbd45e93cbdebdf"],"state_sha256":"799dda1aaf2523bd47080827b398faeb5e6a65974dcb44029aa0c0a32dc54d19"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rN93MjzaOY3jgaMY2+yItjWkdtIeMEylj/LmE4dokOu+LfmAzT/XwhBwn1RXonA8+KN7YCzRWGt081lnaqkhAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T00:35:19.973712Z","bundle_sha256":"ead8f260885e736532944f73da622b02e165d2dad3055cfa9765293685557d71"}}