{"id":"W2742855836","doi":"10.1002/cjs.11327","title":"Minimax robust active learning for approximately specified regression models","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; McGill University; Jewish General Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimax; Statistics; Population; Context (archaeology); Regression; Sampling (signal processing); Regression analysis; Econometrics; Mathematics; Computer science; Benchmark (surveying); Sampling distribution; Mathematical optimization; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004746489,0.0001216502,0.0002394571,0.0001530676,0.0008532503,0.0005767948,0.001006892,0.00005451775,0.00002038263],"category_scores_gemma":[0.000835467,0.000101517,0.0000641775,0.00004185032,0.00007855968,0.0004722424,0.00003735564,0.0003835286,0.000004483807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000839983,"about_ca_system_score_gemma":0.0006736087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008234779,"about_ca_topic_score_gemma":0.0009212729,"domain_scores_codex":[0.9989647,0.00005817679,0.0003094199,0.0001629143,0.0001999874,0.0003048081],"domain_scores_gemma":[0.9978916,0.0001644052,0.0007562949,0.0003229743,0.0004016084,0.0004631245],"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":[0.00006132777,0.00002575764,0.0009171385,0.00006484504,0.00008409657,0.0007277591,0.003537029,0.0536809,0.00004672903,0.1630548,0.02929692,0.7485027],"study_design_scores_gemma":[0.0007096996,0.0002994476,0.004408218,0.0001286589,0.00001774286,0.000131986,0.0001570416,0.9457797,0.000120915,0.03290523,0.01512148,0.0002198136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001722496,0.00005765442,0.9946287,0.0007369046,0.0007176344,0.00007115839,0.00005272213,0.00000708573,0.002005595],"genre_scores_gemma":[0.3659228,0.00001592667,0.632487,0.00005111711,0.0003018974,0.000001580583,0.000005839995,0.00001629716,0.00119752],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8920988,"threshold_uncertainty_score":0.6562598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05832086344198043,"score_gpt":0.2768825962325897,"score_spread":0.2185617327906092,"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."}}