{"id":"W4293275986","doi":"10.48550/arxiv.1602.04450","title":"Bayesian Optimization with Safety Constraints: Safe and Automatic\\n Parameter Tuning in Robotics","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Bayesian optimization; Overshoot (microwave communication); Computer science; Robotics; Process (computing); Context (archaeology); Gaussian process; Robot; Bayesian probability; Set (abstract data type); Artificial intelligence; Probabilistic logic; Optimization problem; Life-critical system; Mathematical optimization; Machine learning; Algorithm; Gaussian; Software; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001924356,0.0003208369,0.000364989,0.0002829803,0.0001117229,0.0002074921,0.0008479496,0.0002441517,0.00004576541],"category_scores_gemma":[0.00004263804,0.0002800013,0.00004992487,0.0004598207,0.0002904634,0.0005801049,0.000803107,0.0003734435,0.000007461896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001458151,"about_ca_system_score_gemma":0.0003019568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001997106,"about_ca_topic_score_gemma":0.00002863197,"domain_scores_codex":[0.9982424,0.0001068447,0.0002652616,0.000927883,0.00009362105,0.0003639829],"domain_scores_gemma":[0.9985864,0.000167238,0.000276758,0.0006932195,0.0001123134,0.0001640951],"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":[0.00002938322,0.0000620188,0.009831858,0.0002111247,0.00006036371,0.0003941304,0.0003187551,0.7281505,0.000002905122,0.2516691,0.00001292738,0.009256878],"study_design_scores_gemma":[0.0006239418,0.00005715128,0.001451566,0.0006627903,0.00002854315,0.00002278525,0.0000469667,0.9627672,0.000008010647,0.03390113,0.00001082301,0.0004191295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003115961,0.00002192643,0.9925023,0.0003842014,0.0001038101,0.0002548054,0.000009635803,0.000134083,0.003473276],"genre_scores_gemma":[0.8598081,0.0001177831,0.1398354,0.00007048774,0.00001493116,8.418218e-7,0.000005261772,0.00001382364,0.0001334356],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8566921,"threshold_uncertainty_score":0.9999652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02897255420827074,"score_gpt":0.1760027893119889,"score_spread":0.1470302351037182,"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."}}