{"id":"W3110902746","doi":"10.1609/aaai.v35i10.17109","title":"Warm Starting CMA-ES for Hyperparameter Optimization","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Institute of Advanced Industrial Science and Technology","keywords":"Bayesian optimization; Hyperparameter; Computer science; Adaptation (eye); Initialization; Machine learning; CMA-ES; Artificial intelligence; Task (project management); Random search; Bayesian probability; Algorithm; Evolution strategy; Evolutionary algorithm","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":[0.001924177,0.002030525,0.00178666,0.0008989936,0.0007663296,0.001371911,0.00172661,0.002035244,0.005520637],"category_scores_gemma":[0.008041586,0.0007929822,0.001360622,0.0008094392,0.001350818,0.001581542,0.002075429,0.003378787,0.001715507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006514092,"about_ca_system_score_gemma":0.001885746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003569639,"about_ca_topic_score_gemma":0.003530051,"domain_scores_codex":[0.9989302,0.0004995769,0.00005416403,0.0002089621,0.000190542,0.0001165682],"domain_scores_gemma":[0.9970892,0.001938933,0.0001767658,0.0002964519,0.0003540804,0.0001446181],"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":[0.0001563557,0.00009306682,0.0005994691,0.0001140835,0.00009317952,0.00009186483,0.00009173525,0.9164827,0.003052871,0.01083109,0.002948506,0.06544506],"study_design_scores_gemma":[0.0000172141,0.00002789643,0.00007969257,0.00001235908,0.000006957373,0.00001301859,0.000009870989,0.9935787,0.0005642329,0.004991591,0.0006894516,0.000008954068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01228857,0.0006657021,0.9825956,0.0002499668,0.000111137,0.00006955714,0.00005115383,0.001008357,0.002959921],"genre_scores_gemma":[0.464156,0.0005167061,0.5270991,0.0009351543,0.0002073536,0.0006910323,0.0005617102,0.0007446367,0.005088343],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005520637,"threshold_uncertainty_score":0.01846838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09431824299911165,"score_gpt":0.31262217584602,"score_spread":0.2183039328469083,"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."}}