{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:QLE64HXUPJWRH452ZJ2VTRPEKK","short_pith_number":"pith:QLE64HXU","schema_version":"1.0","canonical_sha256":"82c9ee1ef47a6d13f3baca7559c5e452a24ecfaeda0e91723d3d3af46d7eeb6b","source":{"kind":"arxiv","id":"1908.10831","version":5},"attestation_state":"computed","paper":{"title":"Stochastic AUC Maximization with Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Mingrui Liu, Tianbao Yang, Yiming Ying, Zhuoning Yuan","submitted_at":"2019-08-28T17:02:49Z","abstract_excerpt":"Stochastic AUC maximization has garnered an increasing interest due to better fit to imbalanced data classification. However, existing works are limited to stochastic AUC maximization with a linear predictive model, which restricts its predictive power when dealing with extremely complex data. In this paper, we consider stochastic AUC maximization problem with a deep neural network as the predictive model. Building on the saddle point reformulation of a surrogated loss of AUC, the problem can be cast into a {\\it non-convex concave} min-max problem. The main contribution made in this paper is t"},"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":"1908.10831","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-28T17:02:49Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"ac35c9ffdfd45bc33b9891fb496139d2d97aecb21ed5286d4cd63259f9c172f6","abstract_canon_sha256":"0ddd4f9f9f97b5cf37f704d9c371f6afb2535167c1ab4720fa411e0adb1aa227"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:14:27.886446Z","signature_b64":"B3E2odLIQTCgrHAQa1qfLY0D++geDsiFUIzt7VYvyq+nuhO4rU2znUk/Mc86wSrYkJG73uTnR16/mJRNceT+CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"82c9ee1ef47a6d13f3baca7559c5e452a24ecfaeda0e91723d3d3af46d7eeb6b","last_reissued_at":"2026-07-05T01:14:27.886041Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:14:27.886041Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Stochastic AUC Maximization with Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Mingrui Liu, Tianbao Yang, Yiming Ying, Zhuoning Yuan","submitted_at":"2019-08-28T17:02:49Z","abstract_excerpt":"Stochastic AUC maximization has garnered an increasing interest due to better fit to imbalanced data classification. However, existing works are limited to stochastic AUC maximization with a linear predictive model, which restricts its predictive power when dealing with extremely complex data. In this paper, we consider stochastic AUC maximization problem with a deep neural network as the predictive model. Building on the saddle point reformulation of a surrogated loss of AUC, the problem can be cast into a {\\it non-convex concave} min-max problem. The main contribution made in this paper is t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.10831","kind":"arxiv","version":5},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1908.10831/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"1908.10831","created_at":"2026-07-05T01:14:27.886107+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.10831v5","created_at":"2026-07-05T01:14:27.886107+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.10831","created_at":"2026-07-05T01:14:27.886107+00:00"},{"alias_kind":"pith_short_12","alias_value":"QLE64HXUPJWR","created_at":"2026-07-05T01:14:27.886107+00:00"},{"alias_kind":"pith_short_16","alias_value":"QLE64HXUPJWRH452","created_at":"2026-07-05T01:14:27.886107+00:00"},{"alias_kind":"pith_short_8","alias_value":"QLE64HXU","created_at":"2026-07-05T01:14:27.886107+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.17338","citing_title":"Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning","ref_index":43,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QLE64HXUPJWRH452ZJ2VTRPEKK","json":"https://pith.science/pith/QLE64HXUPJWRH452ZJ2VTRPEKK.json","graph_json":"https://pith.science/api/pith-number/QLE64HXUPJWRH452ZJ2VTRPEKK/graph.json","events_json":"https://pith.science/api/pith-number/QLE64HXUPJWRH452ZJ2VTRPEKK/events.json","paper":"https://pith.science/paper/QLE64HXU"},"agent_actions":{"view_html":"https://pith.science/pith/QLE64HXUPJWRH452ZJ2VTRPEKK","download_json":"https://pith.science/pith/QLE64HXUPJWRH452ZJ2VTRPEKK.json","view_paper":"https://pith.science/paper/QLE64HXU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.10831&json=true","fetch_graph":"https://pith.science/api/pith-number/QLE64HXUPJWRH452ZJ2VTRPEKK/graph.json","fetch_events":"https://pith.science/api/pith-number/QLE64HXUPJWRH452ZJ2VTRPEKK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QLE64HXUPJWRH452ZJ2VTRPEKK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QLE64HXUPJWRH452ZJ2VTRPEKK/action/storage_attestation","attest_author":"https://pith.science/pith/QLE64HXUPJWRH452ZJ2VTRPEKK/action/author_attestation","sign_citation":"https://pith.science/pith/QLE64HXUPJWRH452ZJ2VTRPEKK/action/citation_signature","submit_replication":"https://pith.science/pith/QLE64HXUPJWRH452ZJ2VTRPEKK/action/replication_record"}},"created_at":"2026-07-05T01:14:27.886107+00:00","updated_at":"2026-07-05T01:14:27.886107+00:00"}