{"id":"W3035283082","doi":"","title":"Linear Lower Bounds and Conditioning of Differentiable Games","year":2020,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Differentiable function; Upper and lower bounds; Convexity; Mathematics; Saddle point; Leverage (statistics); Complement (music); Regular polygon; Saddle; Convex function; Mathematical optimization; Pure mathematics; Mathematical analysis","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":[0.007314319,0.002177398,0.001673073,0.001881409,0.001178041,0.004376818,0.002585136,0.002143377,0.01243537],"category_scores_gemma":[0.04544869,0.0007931563,0.001428788,0.001420199,0.005412675,0.008344068,0.003568623,0.007547845,0.001578262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004838835,"about_ca_system_score_gemma":0.002574283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001638584,"about_ca_topic_score_gemma":0.001745951,"domain_scores_codex":[0.9949039,0.002168464,0.000196696,0.000880555,0.001178954,0.0006715084],"domain_scores_gemma":[0.9601896,0.03281021,0.001983212,0.001899065,0.00211645,0.001001529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007250297,0.00006619842,0.0003897467,0.000145225,0.00002365881,0.00005186379,0.0001439444,0.09947602,0.001027485,0.883565,0.002950731,0.01208771],"study_design_scores_gemma":[0.00001446585,0.00003597981,0.0001368755,0.00006241525,0.000009480027,0.00002674527,0.00002532703,0.4779672,0.0008735776,0.5191054,0.001720087,0.00002244891],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01628256,0.001095505,0.949147,0.002338001,0.0001540324,0.00011475,0.0002443225,0.0003513795,0.03027252],"genre_scores_gemma":[0.7623182,0.002951097,0.2084143,0.001929835,0.0006528756,0.001253853,0.0006083856,0.0007622828,0.02110922],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01243537,"threshold_uncertainty_score":0.04160047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1142328361906052,"score_gpt":0.3897468339893966,"score_spread":0.2755139977987914,"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."}}