{"id":"W3167047574","doi":"","title":"Distributed Second Order Methods with Fast Rates and Compressed Communication","year":2021,"lang":"en","type":"article","venue":"King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology)","topic":"Stochastic Gradient Optimization Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Sublinear function; Hessian matrix; Newton's method; Rate of convergence; Computer science; Mathematical optimization; Regularization (linguistics); Convergence (economics); Local convergence; Algorithm; Quadratic equation; Newton's method in optimization; Iterative method; Applied mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.001042477,0.0002169407,0.0004125359,0.001902884,0.002669641,0.0001126626,0.002116981,0.0002699956,0.000003688468],"category_scores_gemma":[0.0002880474,0.0002469071,0.00002261655,0.008278625,0.02750564,0.001177137,0.002275708,0.0003575766,3.313574e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001349958,"about_ca_system_score_gemma":0.0007776941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001505003,"about_ca_topic_score_gemma":0.00006197952,"domain_scores_codex":[0.9977785,0.00006002663,0.0002099761,0.0009894307,0.0005224528,0.0004396584],"domain_scores_gemma":[0.9955233,0.0001290768,0.0004291381,0.0009400304,0.002841635,0.000136769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006751015,0.0003195324,0.06018245,0.0002052967,0.0001460573,0.0002691834,0.00578421,0.0001178466,0.6320252,0.2413447,0.0001360204,0.05940189],"study_design_scores_gemma":[0.003755258,0.001385968,0.02195535,0.001408839,0.0002783838,0.002154776,0.08901095,0.09809943,0.7581546,0.01725542,0.00474642,0.0017946],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7506772,0.0005475224,0.2436492,0.003204514,0.00007700035,0.0002590661,0.0000077276,0.0005362466,0.001041617],"genre_scores_gemma":[0.7382544,0.0001858483,0.2614541,0.00001498839,0.000001568588,2.136158e-7,0.000001273091,0.000004434288,0.0000831899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2240893,"threshold_uncertainty_score":0.9999983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007462794298355328,"score_gpt":0.2176864445366946,"score_spread":0.2102236502383393,"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."}}