{"id":"W3035691313","doi":"","title":"A simpler approach to accelerated optimization: iterative averaging meets optimism","year":2020,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mathematical optimization; Computer science; Control theory (sociology); Mathematics; Artificial intelligence","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":[0.003851099,0.001998225,0.003002143,0.001499554,0.001032103,0.002469002,0.003413819,0.003074029,0.007831895],"category_scores_gemma":[0.01345294,0.0009346419,0.002273454,0.001475865,0.00207939,0.004342186,0.003849737,0.005089942,0.001939613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001134847,"about_ca_system_score_gemma":0.002073816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002469498,"about_ca_topic_score_gemma":0.002944953,"domain_scores_codex":[0.9969043,0.001226351,0.0001775017,0.0004367565,0.001053357,0.0002015756],"domain_scores_gemma":[0.9968908,0.001308899,0.0001559173,0.0007634279,0.0007452732,0.0001356682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001781077,0.0001555395,0.0003784598,0.0003358962,0.0002190589,0.0001222722,0.0001806055,0.2445894,0.005777474,0.6203905,0.009343829,0.1183289],"study_design_scores_gemma":[0.00002620519,0.00007290001,0.0001156631,0.00001982014,0.00002849222,0.00006965647,0.0000119792,0.858978,0.001280597,0.1338377,0.005526369,0.00003257446],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001269199,0.0002388196,0.9945075,0.0003083416,0.0002369719,0.00002696706,0.00001991524,0.0001279642,0.003264294],"genre_scores_gemma":[0.1443641,0.000822088,0.8365921,0.0007076857,0.001273317,0.0003132677,0.0001486653,0.0007363122,0.01504244],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007831895,"threshold_uncertainty_score":0.02620029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1987136704412686,"score_gpt":0.4037736806013445,"score_spread":0.2050600101600759,"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."}}