{"id":"W2957302152","doi":"10.48550/arxiv.1907.05772","title":"Exploration by Optimisation in Partial Monitoring","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Regret; Observable; Bounded function; Minimax; Upper and lower bounds; Simple (philosophy); Matching (statistics); Action (physics); Degenerate energy levels; Mathematics; Outcome (game theory); Combinatorics; Mathematical optimization; Computer science; Discrete mathematics; Mathematical economics; Statistics; Physics; Mathematical analysis","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":[0.00183184,0.001171531,0.001571906,0.000476547,0.0006500107,0.001300364,0.001681046,0.001385213,0.00362503],"category_scores_gemma":[0.007197698,0.0006077503,0.001014399,0.000725624,0.002069726,0.002668596,0.004299229,0.00203356,0.0007892453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001308261,"about_ca_system_score_gemma":0.001350164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001500095,"about_ca_topic_score_gemma":0.001511241,"domain_scores_codex":[0.9984358,0.0006816162,0.00005467783,0.0003556518,0.0002383702,0.0002339055],"domain_scores_gemma":[0.997557,0.001645968,0.0002025891,0.0003542547,0.00008358788,0.0001564345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006008727,0.0000845823,0.001029097,0.0002076537,0.00008401329,0.0001771251,0.0002743626,0.741108,0.003138767,0.174134,0.003799156,0.0753625],"study_design_scores_gemma":[0.00003816247,0.00004615704,0.0001130582,0.00001686103,0.00001003474,0.00002953473,0.00001402512,0.8829544,0.0007581451,0.1151887,0.0008204581,0.00001038868],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03024005,0.0002756215,0.9612475,0.0005682653,0.00002985443,0.00008722475,0.0001483088,0.0007659914,0.006637188],"genre_scores_gemma":[0.7728041,0.0001992614,0.219605,0.000260101,0.00003857865,0.0003391567,0.0002626285,0.0002183136,0.006272842],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00362503,"threshold_uncertainty_score":0.01212692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.360109314960615,"score_gpt":0.3313054139223404,"score_spread":0.02880390103827463,"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."}}