{"id":"W3017369915","doi":"10.1287/moor.2019.1019","title":"Variance Regularization in Sequential Bayesian Optimization","year":2020,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Regularization (linguistics); Mathematical optimization; Bayesian probability; A priori and a posteriori; Mathematics; Optimization problem; Computer science; Dynamic programming; Algorithm; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006241501,0.001450791,0.001452451,0.001117118,0.000786825,0.002310426,0.001846852,0.002407445,0.004139923],"category_scores_gemma":[0.02565751,0.0009810774,0.001159366,0.00115313,0.003268171,0.003242161,0.002749822,0.00386875,0.0006838461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003348523,"about_ca_system_score_gemma":0.002867101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0077395,"about_ca_topic_score_gemma":0.0066224,"domain_scores_codex":[0.9965001,0.001664309,0.0001182509,0.0004837951,0.0009962743,0.0002372795],"domain_scores_gemma":[0.9884877,0.009253497,0.0006579219,0.0004875265,0.0008737509,0.0002396804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003889094,0.00002370136,0.000540726,0.0001473148,0.00005167604,0.00007315363,0.00008820462,0.4815387,0.0005055535,0.4987009,0.00274425,0.01554685],"study_design_scores_gemma":[0.000008193439,0.0000101234,0.0001061616,0.00003041559,0.000005948071,0.00001290036,0.000009865592,0.7616118,0.0001390726,0.2367556,0.001299583,0.00001046563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004460596,0.0008772681,0.9842715,0.001306416,0.0000718177,0.00003032435,0.0000768768,0.0001543803,0.008750939],"genre_scores_gemma":[0.5479935,0.003068508,0.4206542,0.001727334,0.0006270257,0.0006538157,0.0005505936,0.0006536646,0.02407131],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0077395,"threshold_uncertainty_score":0.03300858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2930472058495573,"score_gpt":0.4985009710732118,"score_spread":0.2054537652236545,"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."}}