{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003753628,0.0001257619,0.0002807725,0.0001023381,0.00007930757,0.0001271599,0.0002389577,0.0000803763,0.0003516489],"category_scores_gemma":[0.0002643449,0.0001178498,0.00005857548,0.0003689455,0.0001066465,0.0001808396,0.00008413378,0.0001713599,0.00002773006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003457231,"about_ca_system_score_gemma":0.00003503435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001317414,"about_ca_topic_score_gemma":0.00005344776,"domain_scores_codex":[0.9987767,0.000003290051,0.0006085163,0.0003517874,0.00006705915,0.0001926173],"domain_scores_gemma":[0.9992611,0.00003113378,0.0003542665,0.0001349789,0.0001796664,0.00003889898],"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":[0.00003600682,0.00005955736,0.02138438,0.00003295595,0.000011323,5.008831e-7,0.0007844011,0.0004569017,0.001004421,0.9740071,0.00003997609,0.002182503],"study_design_scores_gemma":[0.000223388,0.0001432688,0.03216155,0.0005401482,0.00001975305,0.00001394998,0.001684338,0.3510057,0.08006416,0.529511,0.003922546,0.0007101863],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846549,0.0003596722,0.002512226,0.001889755,0.000317888,0.0001534417,0.00003089071,0.00001276558,0.01006843],"genre_scores_gemma":[0.9977453,0.0005897402,0.0008490704,0.0002109753,0.00004629336,0.000007287867,0.000002154737,0.0000103308,0.0005388978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4444961,"threshold_uncertainty_score":0.4805774,"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."}}