{"id":"W2963225502","doi":"10.48550/arxiv.1907.09633","title":"Low-Variance and Zero-Variance Baselines for Extensive-Form Games","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Variance (accounting); Regret; Baseline (sea); Computer science; Sampling (signal processing); Fraction (chemistry); Tree (set theory); Mathematical optimization; Game tree; Importance sampling; Monte Carlo tree search; Counterfactual thinking; Computation; Variance reduction; Monte Carlo method; Mathematics; Algorithm; Repeated game; Statistics; Mathematical economics; Game theory; Machine learning","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":[0.008084529,0.001475805,0.001965652,0.001550755,0.001037212,0.002730016,0.003173781,0.002398471,0.00457022],"category_scores_gemma":[0.04861745,0.000787431,0.001011219,0.001541462,0.002668797,0.006166662,0.003040307,0.004616951,0.0007285581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002527214,"about_ca_system_score_gemma":0.002050745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001858706,"about_ca_topic_score_gemma":0.00276253,"domain_scores_codex":[0.9949763,0.00276503,0.0001886736,0.0008433657,0.0008587826,0.0003678676],"domain_scores_gemma":[0.9807587,0.01442746,0.001140253,0.002031211,0.001059662,0.0005827264],"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":[0.0002221702,0.0001614988,0.001343313,0.0001462548,0.00007319837,0.0001122396,0.0001919432,0.5522112,0.001297596,0.3932366,0.002751245,0.04825266],"study_design_scores_gemma":[0.00002072248,0.00005640792,0.0001847781,0.0000402455,0.000009966792,0.00002581358,0.0000197404,0.7890525,0.0005096539,0.2091707,0.0008905776,0.00001875709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01506614,0.000310801,0.980478,0.000370587,0.00003987376,0.00007198795,0.0001339076,0.000318681,0.003209995],"genre_scores_gemma":[0.6006771,0.0005349478,0.3918372,0.0003829518,0.0001169304,0.0004924698,0.0005043345,0.0003387476,0.005115241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008084529,"threshold_uncertainty_score":0.0427556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07617428834402142,"score_gpt":0.2219628485635528,"score_spread":0.1457885602195314,"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."}}