{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:YVFIFC2DL3H55HVHRTAZ6PDJTR","short_pith_number":"pith:YVFIFC2D","canonical_record":{"source":{"id":"1607.00847","kind":"arxiv","version":9},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-07-04T12:21:04Z","cross_cats_sorted":[],"title_canon_sha256":"e0911400fe01677f7f6d52281c7cdca8133eb5e6a662f92a2670e1f65a540d47","abstract_canon_sha256":"c9e776af34960fac7c8e75eaf5cd44f5ac00e4c7bec0d66eba0cf8925ac81c6b"},"schema_version":"1.0"},"canonical_sha256":"c54a828b435ecfde9ea78cc19f3c699c76e9b040fd391f84fca1908ae36f9ae0","source":{"kind":"arxiv","id":"1607.00847","version":9},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1607.00847","created_at":"2026-05-17T23:51:44Z"},{"alias_kind":"arxiv_version","alias_value":"1607.00847v9","created_at":"2026-05-17T23:51:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.00847","created_at":"2026-05-17T23:51:44Z"},{"alias_kind":"pith_short_12","alias_value":"YVFIFC2DL3H5","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"YVFIFC2DL3H55HVH","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"YVFIFC2D","created_at":"2026-05-18T12:30:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:YVFIFC2DL3H55HVHRTAZ6PDJTR","target":"record","payload":{"canonical_record":{"source":{"id":"1607.00847","kind":"arxiv","version":9},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-07-04T12:21:04Z","cross_cats_sorted":[],"title_canon_sha256":"e0911400fe01677f7f6d52281c7cdca8133eb5e6a662f92a2670e1f65a540d47","abstract_canon_sha256":"c9e776af34960fac7c8e75eaf5cd44f5ac00e4c7bec0d66eba0cf8925ac81c6b"},"schema_version":"1.0"},"canonical_sha256":"c54a828b435ecfde9ea78cc19f3c699c76e9b040fd391f84fca1908ae36f9ae0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:44.667111Z","signature_b64":"bpabiKkzXuMjounfrNB0aezOmGzYGMKOCSdPUI6wThYKFocN6+D6te5OS552arf9gv2tHvavi8m5li96oGFCDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c54a828b435ecfde9ea78cc19f3c699c76e9b040fd391f84fca1908ae36f9ae0","last_reissued_at":"2026-05-17T23:51:44.666472Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:44.666472Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1607.00847","source_version":9,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:51:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J2ZX+5HErOVqPHFAa5+bO6PmFjrusXoCqhRZX881dHvkkXAH2Pq9X2MDSP69Q1+Lq4MLA6F92XAY0Koa9kjQBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T21:55:08.789230Z"},"content_sha256":"1562125f731323756b2c3096a924d3f277244c26d2105ab003b2c8bb2af8609d","schema_version":"1.0","event_id":"sha256:1562125f731323756b2c3096a924d3f277244c26d2105ab003b2c8bb2af8609d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:YVFIFC2DL3H55HVHRTAZ6PDJTR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Confidence-Weighted Bipartite Ranking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hamidreza Chitsaz, Indrakshi Ray, Majdi Khalid","submitted_at":"2016-07-04T12:21:04Z","abstract_excerpt":"Bipartite ranking is a fundamental machine learning and data mining problem. It commonly concerns the maximization of the AUC metric. Recently, a number of studies have proposed online bipartite ranking algorithms to learn from massive streams of class-imbalanced data. These methods suggest both linear and kernel-based bipartite ranking algorithms based on first and second-order online learning. Unlike kernelized ranker, linear ranker is more scalable learning algorithm. The existing linear online bipartite ranking algorithms lack either handling non-separable data or constructing adaptive lar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.00847","kind":"arxiv","version":9},"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":"2026-05-17T23:51:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CzHyKeaVAjXuhRT18RviW9btTCY8IUIviQG9DOHwX03Mh2kabA2f199L0MhlQEWnqT+dcDfjULPAMu/d0JV4Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T21:55:08.789611Z"},"content_sha256":"25e5d50b4aa5b282632eb93567d71450fb0fde6b4549c02486e568d9f617496f","schema_version":"1.0","event_id":"sha256:25e5d50b4aa5b282632eb93567d71450fb0fde6b4549c02486e568d9f617496f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YVFIFC2DL3H55HVHRTAZ6PDJTR/bundle.json","state_url":"https://pith.science/pith/YVFIFC2DL3H55HVHRTAZ6PDJTR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YVFIFC2DL3H55HVHRTAZ6PDJTR/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":"2026-06-22T21:55:08Z","links":{"resolver":"https://pith.science/pith/YVFIFC2DL3H55HVHRTAZ6PDJTR","bundle":"https://pith.science/pith/YVFIFC2DL3H55HVHRTAZ6PDJTR/bundle.json","state":"https://pith.science/pith/YVFIFC2DL3H55HVHRTAZ6PDJTR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YVFIFC2DL3H55HVHRTAZ6PDJTR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:YVFIFC2DL3H55HVHRTAZ6PDJTR","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":"c9e776af34960fac7c8e75eaf5cd44f5ac00e4c7bec0d66eba0cf8925ac81c6b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-07-04T12:21:04Z","title_canon_sha256":"e0911400fe01677f7f6d52281c7cdca8133eb5e6a662f92a2670e1f65a540d47"},"schema_version":"1.0","source":{"id":"1607.00847","kind":"arxiv","version":9}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1607.00847","created_at":"2026-05-17T23:51:44Z"},{"alias_kind":"arxiv_version","alias_value":"1607.00847v9","created_at":"2026-05-17T23:51:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.00847","created_at":"2026-05-17T23:51:44Z"},{"alias_kind":"pith_short_12","alias_value":"YVFIFC2DL3H5","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"YVFIFC2DL3H55HVH","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"YVFIFC2D","created_at":"2026-05-18T12:30:53Z"}],"graph_snapshots":[{"event_id":"sha256:25e5d50b4aa5b282632eb93567d71450fb0fde6b4549c02486e568d9f617496f","target":"graph","created_at":"2026-05-17T23:51:44Z","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":"Bipartite ranking is a fundamental machine learning and data mining problem. It commonly concerns the maximization of the AUC metric. Recently, a number of studies have proposed online bipartite ranking algorithms to learn from massive streams of class-imbalanced data. These methods suggest both linear and kernel-based bipartite ranking algorithms based on first and second-order online learning. Unlike kernelized ranker, linear ranker is more scalable learning algorithm. The existing linear online bipartite ranking algorithms lack either handling non-separable data or constructing adaptive lar","authors_text":"Hamidreza Chitsaz, Indrakshi Ray, Majdi Khalid","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-07-04T12:21:04Z","title":"Confidence-Weighted Bipartite Ranking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.00847","kind":"arxiv","version":9},"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":"sha256:1562125f731323756b2c3096a924d3f277244c26d2105ab003b2c8bb2af8609d","target":"record","created_at":"2026-05-17T23:51:44Z","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":"c9e776af34960fac7c8e75eaf5cd44f5ac00e4c7bec0d66eba0cf8925ac81c6b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-07-04T12:21:04Z","title_canon_sha256":"e0911400fe01677f7f6d52281c7cdca8133eb5e6a662f92a2670e1f65a540d47"},"schema_version":"1.0","source":{"id":"1607.00847","kind":"arxiv","version":9}},"canonical_sha256":"c54a828b435ecfde9ea78cc19f3c699c76e9b040fd391f84fca1908ae36f9ae0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c54a828b435ecfde9ea78cc19f3c699c76e9b040fd391f84fca1908ae36f9ae0","first_computed_at":"2026-05-17T23:51:44.666472Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:51:44.666472Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bpabiKkzXuMjounfrNB0aezOmGzYGMKOCSdPUI6wThYKFocN6+D6te5OS552arf9gv2tHvavi8m5li96oGFCDA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:51:44.667111Z","signed_message":"canonical_sha256_bytes"},"source_id":"1607.00847","source_kind":"arxiv","source_version":9}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1562125f731323756b2c3096a924d3f277244c26d2105ab003b2c8bb2af8609d","sha256:25e5d50b4aa5b282632eb93567d71450fb0fde6b4549c02486e568d9f617496f"],"state_sha256":"111f25cb03e63c09a3714d53bb9394b41a91e432ef41bfa14a815e9dc9de2689"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uZgKCnvGdC/u30F95TXzSBzkDB3c5vRN0ODh+dgqLnvtV52uZLDu6OxGozaRq0+Gy4BRbm75nlSewrnTtKG5DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-22T21:55:08.791496Z","bundle_sha256":"e318c8dcb90c192743e182ee4f659ae5083de358b73e0261870d59393bb351cb"}}