{"id":"W4220750582","doi":"10.1080/01621459.2022.2050243","title":"Sparse Reduced Rank Huber Regression in High Dimensions","year":2022,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of General Medical Sciences; National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Science Foundation","keywords":"Coordinate descent; Mathematics; Rate of convergence; Bounded function; Applied mathematics; Rank (graph theory); Moment (physics); Noise (video); Consistency (knowledge bases); Gaussian; Gaussian noise; Mathematical optimization; Algorithm; Computer science; Combinatorics; Mathematical analysis; Artificial intelligence; Discrete mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003634274,0.00007832988,0.0002414879,0.00008919982,0.00009206087,0.00001469333,0.0001542472,0.00001728624,0.00004053011],"category_scores_gemma":[0.0003198796,0.00005747349,0.00006051734,0.0003244413,0.00002738197,0.00004834475,0.00006728595,0.0004510077,0.000001909069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006041871,"about_ca_system_score_gemma":0.00002835113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006668201,"about_ca_topic_score_gemma":0.000005628547,"domain_scores_codex":[0.9987766,0.0002666269,0.000318772,0.00006339698,0.0004217863,0.0001527942],"domain_scores_gemma":[0.9990567,0.0003227782,0.0004043227,0.0001140357,0.00006412036,0.00003805012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0005297475,0.0005428497,0.04905565,0.00001527405,0.0004400698,0.0002948224,0.001329682,0.1449507,0.1431009,0.005050811,0.6070946,0.04759487],"study_design_scores_gemma":[0.002018361,0.0007723842,0.8740019,0.0002482442,0.0002754098,0.0001743657,0.0009185423,0.05027522,0.01458124,0.04016351,0.0158734,0.0006973696],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950246,0.00004762757,0.003142126,0.0009033614,0.0005260014,0.00006791198,0.00002455173,0.00004136987,0.0002225108],"genre_scores_gemma":[0.9953874,0.00003655528,0.004244918,0.0001800496,0.00006746909,0.000002316493,0.000002118704,0.00001472889,0.00006443125],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8249463,"threshold_uncertainty_score":0.2343701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008938009588913798,"score_gpt":0.2466680270777493,"score_spread":0.2377300174888355,"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."}}