{"id":"W1868011046","doi":"","title":"On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments","year":2015,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Oracle; Set (abstract data type); Exploit; Computer science; Quality (philosophy); Mathematical optimization; Identification (biology); Mathematics; Theoretical computer science","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001635505,0.0002270561,0.0002717685,0.0007307256,0.0001494169,0.0005032125,0.001164321,0.0001079493,0.001681178],"category_scores_gemma":[0.004913196,0.0001949645,0.00007728284,0.0004037331,0.0001057386,0.0004595238,0.0003149684,0.001077588,0.001518475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003816444,"about_ca_system_score_gemma":0.0001287242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001200235,"about_ca_topic_score_gemma":0.0001723231,"domain_scores_codex":[0.994934,0.0004842883,0.000577383,0.0006533775,0.003010767,0.0003402196],"domain_scores_gemma":[0.997745,0.001160356,0.0002749045,0.0003532257,0.0002714167,0.0001951137],"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":[0.0004878809,0.0003502347,0.01452439,0.000002183408,0.00006128407,0.00008246332,0.0007644225,0.8052964,0.001404204,0.1492193,0.0001961542,0.02761107],"study_design_scores_gemma":[0.003407532,0.000467993,0.02456357,0.00009431392,0.000004222292,0.000008363375,0.0009916004,0.4997863,0.0003704277,0.4645422,0.005377375,0.0003861024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5094922,0.0001632385,0.3991223,0.009497384,0.008801813,0.001166225,0.0002092891,0.0002295104,0.07131813],"genre_scores_gemma":[0.9897661,0.00002547849,0.000817506,0.0001392677,0.0001284566,0.00001872127,0.00006554717,0.00002314654,0.009015755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.480274,"threshold_uncertainty_score":0.9992589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2028115132751566,"score_gpt":0.4445514399849025,"score_spread":0.2417399267097459,"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."}}