{"id":"W2964160158","doi":"","title":"Quantifying Learning Guarantees for Convex but Inconsistent Surrogates","year":2018,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Consistency (knowledge bases); Ranking (information retrieval); Computer science; Quadratic equation; Regular polygon; Upper and lower bounds; Mathematical optimization; Machine learning; Tree (set theory); Hinge loss; Computation; Artificial intelligence; Calibration; Function (biology); Mathematics; Algorithm; 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":[0.0003876216,0.0001214345,0.000138936,0.0001284264,0.0004369749,0.0001087271,0.0006267244,0.00006047645,0.00001955072],"category_scores_gemma":[0.0001968974,0.0001289989,0.00007727622,0.000373667,0.0001235884,0.0004635463,0.0001788304,0.0001409682,0.0001642772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003961452,"about_ca_system_score_gemma":0.00004825429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001004323,"about_ca_topic_score_gemma":0.00008307819,"domain_scores_codex":[0.9989289,0.0001160904,0.0001243351,0.0005261377,0.00005347472,0.0002510536],"domain_scores_gemma":[0.9989138,0.0002595354,0.0001314388,0.0004399102,0.0001801903,0.00007511005],"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":[0.0000830651,0.00009091059,0.1097622,0.00005886038,0.00006565198,0.00002224788,0.0006887512,0.003908739,0.003153005,0.8739926,0.0004276604,0.007746282],"study_design_scores_gemma":[0.0006378345,0.000220527,0.01022968,0.00002987372,0.00002224305,0.000006654451,0.0003266041,0.9714941,0.001209118,0.00240115,0.01316716,0.0002550191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.341667,0.0000193287,0.6560887,0.000210369,0.000195131,0.0001049105,0.000002785541,0.000245498,0.001466319],"genre_scores_gemma":[0.9947012,0.00001944931,0.003480307,0.00007011166,0.00007213285,7.132102e-7,0.00001463718,0.000008583141,0.001632864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9675854,"threshold_uncertainty_score":0.5260425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1282507316385844,"score_gpt":0.2263412529550493,"score_spread":0.0980905213164649,"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."}}