{"id":"W2113145584","doi":"","title":"Multi-Task Bayesian Optimization","year":2013,"lang":"en","type":"article","venue":"Digital Access to Scholarship at Harvard (DASH) (Harvard University)","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":452,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Defense Advanced Research Projects Agency","keywords":"Bayesian optimization; Hyperparameter; Computer science; Gaussian process; Machine learning; Bayesian probability; Artificial intelligence; Task (project management); Gaussian","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":[0.003467985,0.001705018,0.002868802,0.001180053,0.000889656,0.00223144,0.00207944,0.003299042,0.01589182],"category_scores_gemma":[0.01153356,0.001378669,0.001242265,0.001798547,0.001533456,0.002329072,0.002274693,0.003221979,0.003693469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001609496,"about_ca_system_score_gemma":0.003685431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01260275,"about_ca_topic_score_gemma":0.01185318,"domain_scores_codex":[0.9987699,0.0005942078,0.00005431941,0.0002847633,0.0001421965,0.0001545621],"domain_scores_gemma":[0.9956326,0.003332843,0.0001777529,0.000189813,0.0004742894,0.0001926713],"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":[0.0004030266,0.0001551188,0.000935645,0.0003293927,0.000186147,0.0001603115,0.00009841257,0.8213432,0.0005054951,0.06021282,0.02912201,0.08654851],"study_design_scores_gemma":[0.00005794598,0.00002956353,0.0001953837,0.00004252603,0.00002040853,0.00002735848,0.00001648645,0.958937,0.0001540475,0.03778923,0.002711743,0.00001839709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009784828,0.002325469,0.9671981,0.002774805,0.0002784506,0.0001789103,0.00109933,0.0007248953,0.01563519],"genre_scores_gemma":[0.4938356,0.002388556,0.4399266,0.001796669,0.0009038614,0.001049384,0.004940798,0.0008598241,0.05429874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01589182,"threshold_uncertainty_score":0.05316341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02370578556113782,"score_gpt":0.2429140372040699,"score_spread":0.2192082516429321,"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."}}