{"id":"W3071135787","doi":"10.1016/j.jat.2020.105472","title":"Recovery guarantees for polynomial coefficients from weakly dependent data with outliers","year":2020,"lang":"en","type":"article","venue":"Journal of Approximation Theory","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Dalhousie University","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Mathematics; Concentration inequality; Mixing (physics); Outlier; Ergodic theory; Applied mathematics; Markov chain; Sampling (signal processing); Matrix (chemical analysis); Polynomial; Function space; Algorithm; Mathematical optimization; Computer science; Discrete mathematics; Mathematical analysis; Statistics; Filter (signal processing)","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":[0.0002584357,0.0001013707,0.0001894404,0.00005861135,0.00003385311,0.00005203961,0.0003294433,0.00004594893,0.00001400232],"category_scores_gemma":[0.00008759453,0.00008057991,0.00004607995,0.00005934354,0.0000227954,0.0002862659,0.00003433025,0.0001316536,0.000002988926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002826561,"about_ca_system_score_gemma":0.00002737108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001304992,"about_ca_topic_score_gemma":8.113866e-7,"domain_scores_codex":[0.9993008,0.00003534128,0.0002797685,0.0001059094,0.000177059,0.0001011459],"domain_scores_gemma":[0.9993745,0.0001216406,0.0001673329,0.000197888,0.00008242974,0.00005622149],"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":[0.01266782,0.0004088354,0.0003599044,0.0002810825,0.002672309,0.0001106932,0.007624141,0.282007,0.2246624,0.002884021,0.1825429,0.2837789],"study_design_scores_gemma":[0.004524261,0.001344731,0.0003749322,0.0006245106,0.0005378133,0.00009437511,0.00268247,0.633632,0.3216671,0.01703843,0.01663201,0.00084738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1446971,0.0002677866,0.854005,0.000162544,0.0002285521,0.0001548034,0.00006884741,0.00009809713,0.0003172003],"genre_scores_gemma":[0.9659047,0.00003379594,0.03347899,0.0001902491,0.0003216543,0.000001894143,0.00003280052,0.00002825171,0.000007651252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8212076,"threshold_uncertainty_score":0.3285954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02863703648723597,"score_gpt":0.2358758877817927,"score_spread":0.2072388512945567,"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."}}