{"id":"W4310446950","doi":"10.1007/s10208-023-09630-x","title":"On the Representation and Learning of Monotone Triangular Transport Maps","year":2023,"lang":"en","type":"article","venue":"Foundations of Computational Mathematics","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Air Force Office of Scientific Research; Multidisciplinary University Research Initiative; Agence Nationale de la Recherche; Natural Sciences and Engineering Research Council of Canada; Institut national de recherche en informatique et en automatique (INRIA); U.S. Department of Energy","keywords":"Maxima and minima; Mathematics; Inference; Parametric statistics; Monotone polygon; Density estimation; Representation (politics); Algorithm; Measure (data warehouse); Applied mathematics; Computer science; Artificial intelligence; Geometry; Data mining","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001241618,0.0001021014,0.0002509639,0.0001789902,0.0001396577,0.00001222226,0.0001143904,0.00004259373,0.0000293078],"category_scores_gemma":[0.001847924,0.00007929066,0.00009281698,0.0004328594,0.0001123534,0.00005675681,0.00002732824,0.0001157675,8.110923e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001175521,"about_ca_system_score_gemma":0.00003864224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005569395,"about_ca_topic_score_gemma":0.000001553131,"domain_scores_codex":[0.9987448,0.0001459518,0.0005184535,0.0001301972,0.0003650659,0.00009551728],"domain_scores_gemma":[0.9936717,0.00553509,0.0003390574,0.0002078218,0.0002185169,0.00002786171],"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":[0.00001662611,0.00009517561,0.00007003276,0.0003087151,0.00007566877,0.000001153631,0.003889217,0.02325646,0.0001347252,0.9706757,0.0007049203,0.000771598],"study_design_scores_gemma":[0.0003456228,0.00006771036,0.0003012416,0.00009701731,0.00005208134,0.000004332703,0.001538667,0.1558062,0.0002647476,0.841273,0.000169768,0.00007959862],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6033158,0.0000105661,0.3930362,0.000539933,0.00009210822,0.0004068061,0.00002458563,0.00005968034,0.002514314],"genre_scores_gemma":[0.7637469,0.00001217612,0.2353121,0.0000182214,0.00003419939,0.00003925896,0.00008737024,0.00002342534,0.0007263177],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1604311,"threshold_uncertainty_score":0.3233379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09728548284479145,"score_gpt":0.3765147759111037,"score_spread":0.2792292930663123,"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."}}