{"id":"W4285364563","doi":"10.1609/aaai.v26i1.8241","title":"Generalized Sampling and Variance in Counterfactual Regret Minimization","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta; Alberta Innovates; Western Canada Research Grid; Compute Canada","keywords":"Estimator; Mathematics; Bias of an estimator; Regret; Counterfactual thinking; Tree traversal; Variance (accounting); Mathematical optimization; Minimum-variance unbiased estimator; Consistent estimator; Sampling (signal processing); Statistics; Computer science; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01862467,0.001202476,0.001905802,0.001531431,0.0009344264,0.002255853,0.002394885,0.001538302,0.001673265],"category_scores_gemma":[0.09424555,0.0009960111,0.001245375,0.00161129,0.003440738,0.00430544,0.002450322,0.002760404,0.0002893633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002846782,"about_ca_system_score_gemma":0.002269592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004846812,"about_ca_topic_score_gemma":0.004689357,"domain_scores_codex":[0.9873046,0.009420256,0.0003385133,0.001042744,0.001448599,0.0004452904],"domain_scores_gemma":[0.9445708,0.04752479,0.001947235,0.003685463,0.001836072,0.0004355824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001826397,0.00006369001,0.002491988,0.00009892878,0.0001377554,0.00008853764,0.0001647003,0.7129517,0.0005166343,0.2538442,0.001083651,0.02837557],"study_design_scores_gemma":[0.00002887564,0.00002939575,0.0002530854,0.00002631,0.00001429212,0.00002440298,0.00001232458,0.9107435,0.0004839459,0.08794025,0.0004294785,0.00001412207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01186549,0.0003524,0.9858278,0.0003347541,0.00003246131,0.00005765569,0.00003165405,0.0001879569,0.001309902],"genre_scores_gemma":[0.560349,0.0005658131,0.4354796,0.0005028445,0.0001650879,0.0005114177,0.0002253477,0.0002762691,0.001924665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01862467,"threshold_uncertainty_score":0.09849787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1337581893652379,"score_gpt":0.2826692167360921,"score_spread":0.1489110273708542,"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."}}