{"id":"W2963067765","doi":"","title":"Structured Best Arm Identification with Fixed Confidence","year":2017,"lang":"en","type":"article","venue":"Algorithmic Learning Theory","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Minimax; Sample complexity; Computer science; Tree (set theory); Action (physics); Identification (biology); Set (abstract data type); Sample (material); Tree structure; Mathematical optimization; Upper and lower bounds; Artificial intelligence; Mathematics; Algorithm; Theoretical computer science; Combinatorics; Binary tree","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007717006,0.0001428593,0.0002471316,0.0001009646,0.000678312,0.0003384669,0.0004481571,0.00007503453,0.0007547195],"category_scores_gemma":[0.0001782426,0.0001385866,0.00005657935,0.00005712178,0.0001844796,0.0003524105,0.00005602072,0.0002764113,0.000593905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004275035,"about_ca_system_score_gemma":0.00002230267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002248355,"about_ca_topic_score_gemma":0.0000186767,"domain_scores_codex":[0.998983,0.00001483782,0.0003402809,0.000371712,0.00005290312,0.0002372416],"domain_scores_gemma":[0.9984406,0.00004133007,0.0007313181,0.0006696698,0.00005298527,0.00006408848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00008636688,0.00008265067,0.1521866,0.00004141316,0.0001780412,0.00002767342,0.001261961,0.003001234,0.00009620898,0.8164613,0.0002101844,0.02636635],"study_design_scores_gemma":[0.002139702,0.0004693196,0.5195154,0.000155472,0.00007542138,0.00006904357,0.0009194135,0.09760875,0.000698153,0.2579474,0.1189965,0.001405425],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8659249,0.001220107,0.07846025,0.0005838245,0.001226354,0.0003561012,0.00005301242,0.0001253719,0.05205005],"genre_scores_gemma":[0.9812742,0.0002174264,0.0004591815,0.00005650291,0.0001884166,0.00001153131,0.00001749477,0.00002704295,0.01774814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5585139,"threshold_uncertainty_score":0.826365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02116394259325816,"score_gpt":0.2317819381336895,"score_spread":0.2106179955404313,"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."}}