{"id":"W3158885076","doi":"","title":"A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets.","year":2021,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Nash equilibrium; Minimax; Artificial neural network; Minimax theorem; Stochastic game; Computer science; Mathematical economics; Reinforcement learning; Game theory; Regular polygon; Mathematical optimization; Class (philosophy); Artificial intelligence; Mathematics","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":[0.0046226,0.002138674,0.001771265,0.0009733142,0.001266527,0.003119087,0.002296192,0.002793706,0.0131977],"category_scores_gemma":[0.02186796,0.0007721257,0.001849872,0.0009071917,0.004976314,0.009314742,0.003749727,0.007088624,0.001500913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002609964,"about_ca_system_score_gemma":0.002099438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002140331,"about_ca_topic_score_gemma":0.001831674,"domain_scores_codex":[0.9983089,0.0007524201,0.00006770231,0.0003973024,0.0002936177,0.0001801235],"domain_scores_gemma":[0.991806,0.006426837,0.0004513357,0.0005128962,0.0004841877,0.0003188036],"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":[0.00005802869,0.00003824308,0.0002754833,0.0002054196,0.00006627672,0.00007133254,0.000141776,0.05416175,0.0005778237,0.9219102,0.01018395,0.01230975],"study_design_scores_gemma":[0.00002162321,0.00004325709,0.0001196503,0.00009419633,0.00001866252,0.00004987286,0.00002615967,0.1837175,0.0003130471,0.8112245,0.004345436,0.00002620147],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008822476,0.001700837,0.9434236,0.005799951,0.0003992537,0.0001240807,0.0003328333,0.0002516275,0.0391455],"genre_scores_gemma":[0.6657813,0.005135547,0.2724434,0.005712948,0.001275977,0.001505782,0.0005433325,0.000850048,0.04675175],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0131977,"threshold_uncertainty_score":0.04415065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1327225173430412,"score_gpt":0.3522322219739912,"score_spread":0.21950970463095,"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."}}