{"id":"W3175018633","doi":"10.18653/v1/2021.findings-acl.106","title":"Minimax and Neyman-Pearson meta-learning for outlier languages","year":2021,"lang":"en","type":"article","venue":"Edinburgh Research Explorer (University of Edinburgh)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Minimax; Outlier; Computer science; Artificial intelligence; Natural language processing; Machine learning; Mathematics; Mathematical optimization","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002324633,0.0001990519,0.000463049,0.0005138848,0.0007098948,0.0001951607,0.0008028427,0.0001330822,0.001914363],"category_scores_gemma":[0.0009619222,0.0002195609,0.000259215,0.000994402,0.000309059,0.0009948829,0.0008328574,0.0005994756,0.000008304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006369549,"about_ca_system_score_gemma":0.0002132459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006845323,"about_ca_topic_score_gemma":0.0000149602,"domain_scores_codex":[0.9971679,0.0002655144,0.0002049622,0.0007479299,0.0009456554,0.0006680398],"domain_scores_gemma":[0.9969833,0.001153722,0.0001276094,0.000496701,0.0009012418,0.0003374004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005284837,0.0008149058,0.003117949,0.0002400368,0.003428844,0.001441059,0.1560539,0.00130836,0.03231678,0.04197706,0.5151615,0.2436112],"study_design_scores_gemma":[0.003125063,0.0007452302,0.001371049,0.0001150042,0.0002997783,0.00004521041,0.1207169,0.06199018,0.002774023,0.00798504,0.8000345,0.0007979432],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.120776,0.00738089,0.8112786,0.02343278,0.001232088,0.001323322,0.00002441744,0.0006291867,0.03392271],"genre_scores_gemma":[0.8457542,0.001261425,0.0945876,0.0002191008,0.0005009671,0.00001817224,0.00004635567,0.00004425259,0.05756789],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7249783,"threshold_uncertainty_score":0.998998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1539053297485818,"score_gpt":0.3444586266272386,"score_spread":0.1905532968786568,"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."}}