{"id":"W2170307371","doi":"","title":"On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning","year":2014,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Frequentist inference; Bayesian optimization; Computer science; Machine learning; Bayesian probability; Artificial intelligence; Constraint (computer-aided design); Feature (linguistics); Function (biology); Mathematical optimization; Bayesian inference; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001027689,0.000152706,0.0001872088,0.000474108,0.0001292304,0.0002927018,0.0002332084,0.00005968089,0.0002514753],"category_scores_gemma":[0.003545202,0.0001246847,0.00001150242,0.0002898249,0.0002261836,0.0001500004,0.00003883908,0.0002407994,0.00007836451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006312464,"about_ca_system_score_gemma":0.00007198884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000268961,"about_ca_topic_score_gemma":0.0002606476,"domain_scores_codex":[0.9977874,0.000159489,0.0004830483,0.0004707915,0.0009197857,0.0001794468],"domain_scores_gemma":[0.9971771,0.001868611,0.0001681678,0.0001638054,0.0005014008,0.0001209432],"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":[0.0001284999,0.00003647511,0.0002756029,0.000002016048,0.000002307793,0.000001726444,0.0001289168,0.6343229,0.00003154029,0.1309274,0.000007707282,0.234135],"study_design_scores_gemma":[0.00009329148,0.0003511424,0.0002989195,0.00004482931,0.000002434735,0.000002225305,0.0001595191,0.8606589,0.0001701428,0.1380725,0.00002229363,0.0001237989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004389176,0.00000202369,0.9934682,0.0007588559,0.0000719133,0.0002859669,0.00008504647,0.00002322086,0.0009156473],"genre_scores_gemma":[0.9234951,0.00001025836,0.07606488,0.0001933581,0.00002124999,0.00003325807,0.00006847082,0.00001021523,0.0001032279],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9191059,"threshold_uncertainty_score":0.5084496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.100663809997761,"score_gpt":0.4084052332996377,"score_spread":0.3077414233018767,"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."}}