{"id":"W2951277631","doi":"","title":"An Adaptive Algorithm for Finite Stochastic Partial Monitoring","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Regret; Logarithm; Minimax; Space (punctuation); Mathematical optimization; Computer science; Algorithm; Mathematics; 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":[0.001648947,0.0008368742,0.001156225,0.0005187293,0.0006068526,0.001560823,0.003124562,0.001668869,0.004107535],"category_scores_gemma":[0.007855116,0.0003677943,0.0007997497,0.0007828156,0.001203739,0.002938385,0.002249702,0.002284571,0.0008941171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001482612,"about_ca_system_score_gemma":0.001850946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001473534,"about_ca_topic_score_gemma":0.002116821,"domain_scores_codex":[0.9982994,0.0004397177,0.00009833226,0.0005422741,0.0003905913,0.0002297528],"domain_scores_gemma":[0.9972959,0.001333229,0.0003133194,0.0006316173,0.0002219478,0.0002040332],"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":[0.0007641502,0.0003724808,0.002826853,0.0002251654,0.0001379294,0.0001900949,0.0003391331,0.4474258,0.009705434,0.2436618,0.01015743,0.2841938],"study_design_scores_gemma":[0.00006761963,0.00005091227,0.000131346,0.00001008764,0.00001553839,0.00006263039,0.00001316976,0.9419195,0.001521392,0.05480787,0.001385614,0.00001424306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01714206,0.0001161392,0.9781525,0.000411513,0.00006263906,0.00009033755,0.00008621345,0.0008643264,0.00307437],"genre_scores_gemma":[0.4669216,0.0001200143,0.5263615,0.0003918025,0.0001004419,0.0003464067,0.0003024437,0.0002140197,0.005241694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004107535,"threshold_uncertainty_score":0.01374108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3480788863386984,"score_gpt":0.3467574326496035,"score_spread":0.001321453689094865,"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."}}