{"id":"W2909800602","doi":"10.3929/ethz-b-000337731","title":"No-Regret Bayesian Optimization with Unknown Hyperparameters","year":2019,"lang":"en","type":"article","venue":"Repository for Publications and Research Data (ETH Zurich)","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Vector Institute; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Hyperparameter; Bayesian optimization; Computer science; Regret; Benchmark (surveying); Gaussian process; Hyperparameter optimization; Mathematical optimization; Convergence (economics); Machine learning; Kernel (algebra); Black box; Artificial intelligence; Function (biology); Bayesian probability; Algorithm; Gaussian; Mathematics; Support vector machine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005600883,0.002503819,0.003013983,0.0009182172,0.0009337163,0.002101297,0.002678205,0.003313641,0.003713763],"category_scores_gemma":[0.0227097,0.001151678,0.001162164,0.001463205,0.002405806,0.002676132,0.00253611,0.004253664,0.001391775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00229548,"about_ca_system_score_gemma":0.003537363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009218817,"about_ca_topic_score_gemma":0.009719875,"domain_scores_codex":[0.9970313,0.001630445,0.00011714,0.0004991216,0.0004354438,0.0002864373],"domain_scores_gemma":[0.9870472,0.01029695,0.0007704608,0.0008651704,0.0006886208,0.000331655],"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":[0.000235395,0.0001147257,0.0007621924,0.0001739117,0.00006967899,0.00005046448,0.00005732545,0.9189519,0.0004801429,0.03182211,0.004537834,0.04274436],"study_design_scores_gemma":[0.00002702033,0.00001755541,0.00008093937,0.00001650229,0.000007021047,0.00001138439,0.00000737373,0.983528,0.0002248196,0.01566289,0.0004100949,0.000006397262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01169716,0.0007680561,0.9823453,0.0008966452,0.00004974557,0.0000840803,0.0001325346,0.0008122732,0.003214105],"genre_scores_gemma":[0.4624254,0.0008378966,0.5257748,0.001056388,0.0002225158,0.0005568993,0.0008892405,0.0007712572,0.007465585],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009218817,"threshold_uncertainty_score":0.02962065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2288060053411779,"score_gpt":0.4751225414535335,"score_spread":0.2463165361123556,"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."}}