{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:KCRS263JX3MLZCCBW2V24IKVVJ","short_pith_number":"pith:KCRS263J","schema_version":"1.0","canonical_sha256":"50a32d7b69bed8bc8841b6abae2155aa52116dc6e3fc010d11cda65efe8ed89f","source":{"kind":"arxiv","id":"1906.06053","version":1},"attestation_state":"computed","paper":{"title":"Stochastic Proximal AUC Maximization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Yiming Ying, Yunwen Lei","submitted_at":"2019-06-14T07:16:26Z","abstract_excerpt":"In this paper we consider the problem of maximizing the Area under the ROC curve (AUC) which is a widely used performance metric in imbalanced classification and anomaly detection. Due to the pairwise nonlinearity of the objective function, classical SGD algorithms do not apply to the task of AUC maximization. We propose a novel stochastic proximal algorithm for AUC maximization which is scalable to large scale streaming data. Our algorithm can accommodate general penalty terms and is easy to implement with favorable $O(d)$ space and per-iteration time complexities. We establish a high-probabi"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1906.06053","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-14T07:16:26Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"adf87fcc5d8df1f899694d41c2065650628b680ece60facc0f085f9490df4236","abstract_canon_sha256":"4f3efdf07f8d22ecce0f18e1cd9b7a5cef320e8e9103beca2dd2b67165563004"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:19.897737Z","signature_b64":"s4ooNSFu1vJ0q7BJMiO6zjG836jpFXHdHHnK/6npZxnpy38marGTi9lBAmv3ODsn1BEHoWaEcvnrYtqKPTV2Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"50a32d7b69bed8bc8841b6abae2155aa52116dc6e3fc010d11cda65efe8ed89f","last_reissued_at":"2026-05-17T23:43:19.897108Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:19.897108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Stochastic Proximal AUC Maximization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Yiming Ying, Yunwen Lei","submitted_at":"2019-06-14T07:16:26Z","abstract_excerpt":"In this paper we consider the problem of maximizing the Area under the ROC curve (AUC) which is a widely used performance metric in imbalanced classification and anomaly detection. Due to the pairwise nonlinearity of the objective function, classical SGD algorithms do not apply to the task of AUC maximization. We propose a novel stochastic proximal algorithm for AUC maximization which is scalable to large scale streaming data. Our algorithm can accommodate general penalty terms and is easy to implement with favorable $O(d)$ space and per-iteration time complexities. We establish a high-probabi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.06053","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1906.06053","created_at":"2026-05-17T23:43:19.897218+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.06053v1","created_at":"2026-05-17T23:43:19.897218+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.06053","created_at":"2026-05-17T23:43:19.897218+00:00"},{"alias_kind":"pith_short_12","alias_value":"KCRS263JX3ML","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"KCRS263JX3MLZCCB","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"KCRS263J","created_at":"2026-05-18T12:33:21.387695+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KCRS263JX3MLZCCBW2V24IKVVJ","json":"https://pith.science/pith/KCRS263JX3MLZCCBW2V24IKVVJ.json","graph_json":"https://pith.science/api/pith-number/KCRS263JX3MLZCCBW2V24IKVVJ/graph.json","events_json":"https://pith.science/api/pith-number/KCRS263JX3MLZCCBW2V24IKVVJ/events.json","paper":"https://pith.science/paper/KCRS263J"},"agent_actions":{"view_html":"https://pith.science/pith/KCRS263JX3MLZCCBW2V24IKVVJ","download_json":"https://pith.science/pith/KCRS263JX3MLZCCBW2V24IKVVJ.json","view_paper":"https://pith.science/paper/KCRS263J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.06053&json=true","fetch_graph":"https://pith.science/api/pith-number/KCRS263JX3MLZCCBW2V24IKVVJ/graph.json","fetch_events":"https://pith.science/api/pith-number/KCRS263JX3MLZCCBW2V24IKVVJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KCRS263JX3MLZCCBW2V24IKVVJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KCRS263JX3MLZCCBW2V24IKVVJ/action/storage_attestation","attest_author":"https://pith.science/pith/KCRS263JX3MLZCCBW2V24IKVVJ/action/author_attestation","sign_citation":"https://pith.science/pith/KCRS263JX3MLZCCBW2V24IKVVJ/action/citation_signature","submit_replication":"https://pith.science/pith/KCRS263JX3MLZCCBW2V24IKVVJ/action/replication_record"}},"created_at":"2026-05-17T23:43:19.897218+00:00","updated_at":"2026-05-17T23:43:19.897218+00:00"}