{"id":"W2243169878","doi":"10.1109/camsap.2015.7383850","title":"Multi-agent mirror descent for decentralized stochastic optimization","year":2015,"lang":"en","type":"article","venue":"","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Stochastic gradient descent; Descent (aeronautics); Mathematical optimization; Multi-agent system; Stochastic optimization; Distributed computing; Artificial intelligence; Mathematics; Engineering; Artificial neural network; Aerospace engineering","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":[0.0009526019,0.0005047623,0.0007758925,0.0002942917,0.000388912,0.0005649099,0.0009856513,0.0008326033,0.001354444],"category_scores_gemma":[0.002197405,0.0003249259,0.0004595614,0.000301137,0.0006335172,0.0006941222,0.001077413,0.000960336,0.0003676321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006175175,"about_ca_system_score_gemma":0.001287954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001526625,"about_ca_topic_score_gemma":0.001911805,"domain_scores_codex":[0.9995795,0.0001433333,0.0000150139,0.00006511785,0.0001648817,0.00003211655],"domain_scores_gemma":[0.9993398,0.0002945309,0.00009038705,0.0000796313,0.0001452873,0.00005026217],"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":[0.00004845266,0.0000450758,0.0002985137,0.00005341354,0.00003570625,0.00005467666,0.0000389399,0.9147733,0.003526038,0.04866553,0.001211138,0.03124915],"study_design_scores_gemma":[0.00000357286,0.000008705179,0.0000134011,7.187384e-7,8.431521e-7,0.000003797987,7.947314e-7,0.9965497,0.000212539,0.0030063,0.0001984468,0.000001160417],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00369432,0.00003256491,0.9952696,0.00006624753,0.0000150803,0.00001406517,0.000007377296,0.00008995336,0.0008107682],"genre_scores_gemma":[0.4721167,0.0001293025,0.522787,0.0001256203,0.00006926058,0.0002567189,0.00008673291,0.0001062568,0.00432231],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001526625,"threshold_uncertainty_score":0.005037904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08635434235341079,"score_gpt":0.2995954519515165,"score_spread":0.2132411095981058,"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."}}