{"id":"W3132892856","doi":"","title":"Comparing Inverse Optimization and Machine Learning Methods for Imputing a Convex Objective Function","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Bayesian optimization; Correctness; Optimization problem; Gaussian process; Set (abstract data type); Support vector machine; Mathematical optimization; Algorithm; Gaussian; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0325195,0.00216012,0.002504393,0.003055053,0.0007590742,0.002773626,0.002679424,0.003834521,0.002022493],"category_scores_gemma":[0.122689,0.000881754,0.001538901,0.002480211,0.004362571,0.00653864,0.00399068,0.006438663,0.0007066089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002492942,"about_ca_system_score_gemma":0.002674424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00526569,"about_ca_topic_score_gemma":0.004027511,"domain_scores_codex":[0.9860737,0.009206608,0.0004798152,0.001324951,0.002605037,0.0003098325],"domain_scores_gemma":[0.8568693,0.1284521,0.003385144,0.007324781,0.003239765,0.0007288595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000646887,0.0002925349,0.005966099,0.0006554162,0.0005287946,0.00005722327,0.0002908466,0.6623296,0.0006999291,0.1742969,0.003317385,0.1509184],"study_design_scores_gemma":[0.00004927391,0.0001113719,0.000722262,0.00008310712,0.00003119651,0.00003109579,0.00004710865,0.9185306,0.0006439358,0.07857794,0.00114628,0.00002587051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02588686,0.004676425,0.9604145,0.002581405,0.0001757982,0.0001212821,0.00008305418,0.0005076339,0.005553071],"genre_scores_gemma":[0.4006957,0.004111527,0.5891098,0.00130892,0.0005152981,0.0004843073,0.0005679664,0.000636834,0.0025696],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0325195,"threshold_uncertainty_score":0.1719816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08459372874696396,"score_gpt":0.2287378316225223,"score_spread":0.1441441028755583,"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."}}