{"id":"W2604713477","doi":"10.48550/arxiv.1703.02626","title":"Horde of Bandits using Gaussian Markov Random Fields","year":2017,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Thompson sampling; Computer science; Scalability; Cluster analysis; Regret; Markov chain; Graph; Gaussian; Recommender system; Theoretical computer science; Mathematical optimization; Artificial intelligence; Algorithm; Machine learning; Mathematics","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":[0.004464697,0.00129547,0.002134654,0.001198178,0.001134929,0.001886863,0.002436833,0.002213366,0.003824665],"category_scores_gemma":[0.01881774,0.0008315694,0.001088554,0.00125933,0.002592875,0.003099775,0.00205043,0.003312971,0.0008205805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002262726,"about_ca_system_score_gemma":0.001776401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006160742,"about_ca_topic_score_gemma":0.006726993,"domain_scores_codex":[0.9975225,0.001555879,0.00006003195,0.0003432782,0.000340026,0.0001782126],"domain_scores_gemma":[0.9902715,0.007506056,0.0005206654,0.0009191128,0.0004412463,0.0003412618],"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":[0.0002763999,0.00009780913,0.0009281501,0.00009462391,0.00007755304,0.00009532049,0.00008966333,0.7926645,0.0007931842,0.1715745,0.00413219,0.0291762],"study_design_scores_gemma":[0.00001775243,0.00001937445,0.00006113422,0.00001000273,0.000006309663,0.00001278826,0.000006905189,0.9568261,0.0001566039,0.04238484,0.0004918024,0.000006467552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03393482,0.0009726092,0.9575313,0.001312717,0.0001371731,0.0001063189,0.0001606002,0.0005997582,0.005244709],"genre_scores_gemma":[0.7248514,0.00120496,0.2622168,0.0009807985,0.0003073987,0.0004511639,0.000416966,0.0003416497,0.009228836],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006160742,"threshold_uncertainty_score":0.0236119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2722265457874038,"score_gpt":0.3314936328505302,"score_spread":0.05926708706312639,"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."}}