{"id":"W4379805925","doi":"10.2514/6.2023-4261","title":"Efficient Acquisition Functions for Bayesian Optimization in the Presence of Hidden Constraints","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; McGill University","funders":"","keywords":"Bayesian optimization; Computer science; Bayesian probability; Classifier (UML); Artificial intelligence; Machine learning; Test functions for optimization; Optimization problem; Convergence (economics); Context (archaeology); Mathematical optimization; Algorithm; Multi-swarm optimization; 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":[0.004078615,0.001393267,0.001514034,0.0009985341,0.0004549768,0.001472035,0.00116569,0.001712307,0.003682688],"category_scores_gemma":[0.01133482,0.000923046,0.0009501876,0.0007908986,0.001135981,0.001752756,0.00218295,0.002560447,0.001106757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009682379,"about_ca_system_score_gemma":0.001841945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002008555,"about_ca_topic_score_gemma":0.002238908,"domain_scores_codex":[0.9987904,0.0005776479,0.00005531531,0.0001068701,0.0003844103,0.00008524306],"domain_scores_gemma":[0.9962405,0.002959092,0.000221081,0.0001462783,0.0003567694,0.00007619428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000125594,0.00007629493,0.0006577552,0.0002503507,0.00005831644,0.00008698227,0.0001483382,0.7773017,0.005148575,0.0689361,0.001730983,0.1454789],"study_design_scores_gemma":[0.000004491497,0.00001950969,0.00005598533,0.00001659525,0.000004171181,0.00001418224,0.000005657472,0.9912434,0.0006073308,0.007522054,0.0005019748,0.000004647146],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001597169,0.00006413095,0.9975802,0.00005274212,0.000004873763,0.00001695515,0.000007810164,0.00006118466,0.0006148635],"genre_scores_gemma":[0.1844973,0.0004938071,0.8105323,0.0001444693,0.00005287074,0.0005281056,0.0001580025,0.0002847748,0.003308333],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004078615,"threshold_uncertainty_score":0.02157003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0170634122639862,"score_gpt":0.2759337185527743,"score_spread":0.258870306288788,"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."}}