{"id":"W2965180428","doi":"10.24963/ijcai.2019/474","title":"Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Government of Jiangsu Province; National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Metric (unit); Maximization; Benchmark (surveying); Scalability; Artificial intelligence; Machine learning; Stochastic optimization; Generalization; Stochastic gradient descent; Nonlinear system; Expectation–maximization algorithm; Mathematical optimization; Optimization problem; Supervised learning; Algorithm; Mathematics; Artificial neural network; Maximum likelihood; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002421441,0.001580689,0.001829553,0.0007520255,0.0005788766,0.001061722,0.001625009,0.00180962,0.001731875],"category_scores_gemma":[0.008048321,0.0007776935,0.0009401776,0.0007660763,0.001705015,0.001547265,0.001989166,0.002460337,0.0008463371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001193248,"about_ca_system_score_gemma":0.001824117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003855968,"about_ca_topic_score_gemma":0.004934177,"domain_scores_codex":[0.9989311,0.0004676065,0.00005393651,0.0002203239,0.0002449715,0.00008204062],"domain_scores_gemma":[0.9971207,0.001749777,0.0002484621,0.0003052268,0.0004226334,0.000153123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001043532,0.00007866693,0.0006814828,0.0001295829,0.00005773091,0.00008337197,0.00008203308,0.9120088,0.002637708,0.01679027,0.003168589,0.0641774],"study_design_scores_gemma":[0.000004743687,0.000008408173,0.00002698721,0.000002697073,0.000001241974,0.000008774357,0.000002448414,0.9954402,0.0002446104,0.004102008,0.00015513,0.000002680119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008786799,0.0002238947,0.9890913,0.0002423582,0.00002569958,0.00004825334,0.00004707621,0.0008109464,0.0007237163],"genre_scores_gemma":[0.43401,0.0003114411,0.5603848,0.0004805065,0.0001149174,0.0004033918,0.0005437169,0.0006250945,0.003126156],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003855968,"threshold_uncertainty_score":0.01280594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01369790377065635,"score_gpt":0.2652193181752698,"score_spread":0.2515214144046135,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}