{"id":"W3011628187","doi":"10.1109/sampta45681.2019.9030814","title":"Non-Gaussian Random Matrices on Sets:Optimal Tail Dependence and Applications","year":2019,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gaussian; Random matrix; Dimension (graph theory); Dependency (UML); Algorithm; Computer science; Gaussian process; Gaussian random field; Set (abstract data type); Matrix (chemical analysis); Gaussian function; Mathematics; Artificial intelligence; Combinatorics; Physics; Eigenvalues and eigenvectors","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":[0.00003448228,0.00008970385,0.000101027,0.00005401425,0.00002998296,0.00003652297,0.00008314323,0.00004707619,0.00006375606],"category_scores_gemma":[0.000001189171,0.0000765352,0.00002022551,0.00006936282,0.00001199175,0.00006642809,0.00002168941,0.00008153894,0.0001573518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009527499,"about_ca_system_score_gemma":0.000003561635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001734287,"about_ca_topic_score_gemma":0.00000367986,"domain_scores_codex":[0.9995991,0.00000392311,0.00008141645,0.0001288146,0.0000734524,0.0001132812],"domain_scores_gemma":[0.9997049,0.00003531874,0.00001269988,0.0001975692,0.00001240165,0.000037102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005106065,0.0003726891,0.02497539,0.0006119272,0.0006168111,0.00007911741,0.001302378,0.1680501,0.2601479,0.05002894,0.08687387,0.4064303],"study_design_scores_gemma":[0.003448288,0.0002968998,0.01105904,0.0002771489,0.00008450194,0.00008608666,0.0003472171,0.6315782,0.2764226,0.003243258,0.07168081,0.001475954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6896819,0.0004455076,0.1343736,0.0001098089,0.0001242823,0.0008608867,0.000005613974,0.001617377,0.172781],"genre_scores_gemma":[0.9942174,0.0001095119,0.005234905,0.0000622489,0.00003061227,0.00002784242,0.000002313449,0.00001473849,0.0003004333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4635281,"threshold_uncertainty_score":0.3121015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006710938321422676,"score_gpt":0.2213067641868149,"score_spread":0.2145958258653922,"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."}}