{"id":"W3034189935","doi":"","title":"Online mirror descent and dual averaging: keeping pace in the dynamic case","year":2020,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Dual (grammatical number); Pace; Computer science; Stochastic gradient descent; Descent (aeronautics); Artificial intelligence; Engineering; Aerospace engineering; Geodesy; Artificial neural network; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.0002165972,0.0001030154,0.00009237027,0.00008676943,0.0001309189,0.0001219162,0.0005106323,0.00004734077,0.00002117578],"category_scores_gemma":[0.0001093526,0.00008465782,0.00002199593,0.0001808664,0.00004747509,0.00009278503,0.0002107387,0.0006675482,0.00001091551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002611718,"about_ca_system_score_gemma":0.0000243193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001685902,"about_ca_topic_score_gemma":0.0002550007,"domain_scores_codex":[0.9991358,0.00009609096,0.0001575493,0.0003097045,0.0001697367,0.0001311172],"domain_scores_gemma":[0.9995832,0.000099148,0.00007408033,0.0001539974,0.00004908833,0.00004044679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000284544,0.000259316,0.01184765,0.00001696221,0.00003470527,0.001373466,0.008673172,0.0006748293,0.001386557,0.8481163,0.00002485049,0.1275637],"study_design_scores_gemma":[0.0002662728,0.00006655294,0.00438396,0.00001827031,0.000001934214,0.000404057,0.0003275678,0.9898592,0.00002536039,0.002748533,0.001797301,0.0001009722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7906391,0.0000824818,0.08842393,0.1196703,0.00006796658,0.0001628056,0.000007947138,0.0001565507,0.0007888797],"genre_scores_gemma":[0.9927182,0.00004265617,0.005196075,0.001976464,0.00001465142,0.00001291891,0.00001275683,0.000004639428,0.0000216932],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9891844,"threshold_uncertainty_score":0.3452246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03884420766586107,"score_gpt":0.301901075546146,"score_spread":0.2630568678802849,"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."}}