{"id":"W2622730215","doi":"","title":"Discovering and Exploiting Additive Structure for Bayesian Optimization","year":2017,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Computer science; Bayesian probability; Bayesian optimization; Artificial intelligence; Data mining; Machine learning","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":[0.00674759,0.001427,0.002583228,0.002336987,0.0009225053,0.002247482,0.0027786,0.002375441,0.002231469],"category_scores_gemma":[0.03728081,0.002785833,0.002390448,0.001755982,0.002436262,0.004702394,0.003943884,0.004171262,0.0005443043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001276607,"about_ca_system_score_gemma":0.002475635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006460345,"about_ca_topic_score_gemma":0.008034138,"domain_scores_codex":[0.9968581,0.001750075,0.0002027161,0.0004165962,0.0005990667,0.0001734341],"domain_scores_gemma":[0.9783079,0.01859835,0.0007620562,0.001094021,0.0009252363,0.0003124315],"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":[0.000161914,0.0001555325,0.001416661,0.0001949492,0.0002872112,0.00006638261,0.0002418591,0.7127374,0.001312959,0.1574108,0.00207352,0.1239408],"study_design_scores_gemma":[0.00001049569,0.0000149712,0.0000891351,0.00001119333,0.00001526749,0.000007163268,0.000007673947,0.9250748,0.0001989454,0.07425996,0.0002999627,0.00001044596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00484252,0.0001799478,0.9942932,0.0001767617,0.0000136434,0.00001408672,0.00002544258,0.0001048249,0.0003495106],"genre_scores_gemma":[0.298718,0.0007254606,0.697022,0.0003147929,0.0001828028,0.0001922144,0.0003868691,0.000230659,0.002227196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00674759,"threshold_uncertainty_score":0.03568506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.074198638650729,"score_gpt":0.3368367995968381,"score_spread":0.2626381609461091,"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."}}