{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02031492,0.002182858,0.002976916,0.002033138,0.001114775,0.004189377,0.003642729,0.003841144,0.002839159],"category_scores_gemma":[0.1464296,0.001428866,0.00166836,0.002144538,0.004984601,0.008708444,0.006574369,0.007454975,0.0007140076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002387858,"about_ca_system_score_gemma":0.001808467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007497847,"about_ca_topic_score_gemma":0.0004993218,"domain_scores_codex":[0.9868712,0.00637263,0.0006128823,0.001697651,0.00380256,0.0006429037],"domain_scores_gemma":[0.8922011,0.08101255,0.007674771,0.01069097,0.006719809,0.001700806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000253882,0.0001220371,0.004139994,0.0003685092,0.0001399037,0.0002460123,0.00031262,0.5938603,0.003850754,0.359901,0.002547379,0.03425777],"study_design_scores_gemma":[0.00001750736,0.00008137676,0.0003520124,0.00005705093,0.00001358756,0.00009030663,0.00002889215,0.7839725,0.001365064,0.2133844,0.0006155386,0.00002176411],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01814091,0.0004685598,0.9789268,0.0007792409,0.00004484366,0.00002696146,0.00007059219,0.0001544881,0.001387513],"genre_scores_gemma":[0.6372679,0.001051091,0.3550075,0.0008127396,0.0004043443,0.0004013035,0.0008811291,0.0008983235,0.003275494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02031492,"threshold_uncertainty_score":0.1074368,"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."}}