{"id":"W2997060212","doi":"10.1609/aaai.v34i04.6029","title":"Quadruply Stochastic Gradient Method for Large Scale Nonlinear Semi-Supervised Ordinal Regression AUC Optimization","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Ordinal regression; Generalization; Computer science; Benchmark (surveying); Scalability; Ranking (information retrieval); Ordinal Scale; Ordinal data; Regression; Binary classification; Scale (ratio); Mathematics; Mathematical optimization; Artificial intelligence; Algorithm; Machine learning; Statistics; Support vector machine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007298411,0.0002301737,0.0002798285,0.0000924912,0.0003173409,0.0002420273,0.001582038,0.00009582492,0.00003986173],"category_scores_gemma":[0.001066776,0.0001685019,0.0001279776,0.0006706772,0.00006568674,0.0004076669,0.0003197799,0.0002977124,0.00003028628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003484809,"about_ca_system_score_gemma":0.00009503067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001752882,"about_ca_topic_score_gemma":0.000003530983,"domain_scores_codex":[0.9979867,0.00004280147,0.0005314521,0.0006654637,0.0004402503,0.0003332891],"domain_scores_gemma":[0.9982899,0.0001525282,0.000444361,0.000299885,0.0006592916,0.0001540007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005553021,0.0004480322,0.0002071714,0.00027412,0.00003507021,3.970545e-7,0.009144139,0.03279791,0.05617733,0.7698917,0.001047825,0.129421],"study_design_scores_gemma":[0.0000717121,0.0002983304,0.00002137635,0.0001427992,0.00001793666,0.00000184415,0.0005666272,0.9461736,0.04694096,0.005279292,0.0003014336,0.0001840976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003660196,0.00001327931,0.9821104,0.01280708,0.0002185421,0.0005411312,0.00002541382,0.0001315551,0.0004924072],"genre_scores_gemma":[0.7287692,0.000009771146,0.2702464,0.0006690028,0.0001450956,0.00005665019,0.00001216725,0.00001844483,0.0000732207],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9133757,"threshold_uncertainty_score":0.6871309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07678475938076705,"score_gpt":0.3323505883309635,"score_spread":0.2555658289501965,"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."}}