{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006423134,0.002039708,0.00432569,0.00142226,0.000684702,0.002825204,0.003536338,0.004752644,0.006733037],"category_scores_gemma":[0.03996534,0.001260499,0.001383373,0.001698665,0.003033155,0.004582913,0.003822386,0.004421545,0.00118205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001445645,"about_ca_system_score_gemma":0.001822186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001888934,"about_ca_topic_score_gemma":0.001669574,"domain_scores_codex":[0.996203,0.001621869,0.0001611052,0.0009992967,0.0006090203,0.0004057178],"domain_scores_gemma":[0.9696751,0.02394114,0.003118138,0.001655061,0.0007221887,0.0008882663],"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":[0.0002739446,0.000108045,0.001733045,0.0001530325,0.0001098794,0.0001566731,0.00009826648,0.8777383,0.0005197519,0.09848912,0.00124291,0.019377],"study_design_scores_gemma":[0.00003728606,0.0000736126,0.0002060095,0.00002714565,0.00001296299,0.00002933482,0.00001563298,0.9107742,0.0002382745,0.08824668,0.0003219295,0.00001680534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04979363,0.0004005427,0.9437644,0.001084996,0.00004210864,0.00009904213,0.0003150279,0.0002846447,0.004215449],"genre_scores_gemma":[0.8241048,0.0004304643,0.1644064,0.0005733828,0.0001442408,0.0004479065,0.0008929382,0.0001620572,0.008837879],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006733037,"threshold_uncertainty_score":0.03396916,"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."}}