{"id":"W2949048600","doi":"10.48550/arxiv.1906.01235","title":"Universal Boosting Variational Inference","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mathematics; Mathematical optimization; Hellinger distance; Boosting (machine learning); Degeneracy (biology); Applied mathematics; Inference; Algorithm; Computer science; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003783595,0.0003108436,0.0004341449,0.0001500586,0.0001091228,0.00005863658,0.0006225436,0.0003533648,0.0007470557],"category_scores_gemma":[0.001886888,0.0003505265,0.0001514183,0.0002425517,0.0001138072,0.0001075952,0.0009637208,0.0007589444,0.0001811463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002208161,"about_ca_system_score_gemma":0.0003746724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001193306,"about_ca_topic_score_gemma":0.00001428501,"domain_scores_codex":[0.9983028,0.0002385886,0.0002535131,0.0007511824,0.0001249324,0.0003289827],"domain_scores_gemma":[0.9957621,0.002804969,0.0003346093,0.0006818376,0.0002719299,0.000144517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002851087,0.00006047386,0.004096573,0.0001603825,0.00007635581,0.00006374484,0.00009571113,0.01290807,0.00001465628,0.9818281,0.0003264534,0.0003409864],"study_design_scores_gemma":[0.0003119446,0.00003667138,0.002769958,0.0001642464,0.0001292306,0.000001384023,0.00007493782,0.2306739,0.00001336863,0.7652655,0.0001909632,0.0003678286],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08486807,0.000008710315,0.887018,0.00003651598,0.0005380985,0.0002701972,0.000126995,0.0001430244,0.02699042],"genre_scores_gemma":[0.927497,0.00002698847,0.06953994,0.0000486619,0.00009897396,5.362581e-7,0.00002406067,0.00002699857,0.002736838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.842629,"threshold_uncertainty_score":0.9998947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2337198506967876,"score_gpt":0.2784144575062705,"score_spread":0.04469460680948292,"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."}}