{"id":"W1871676304","doi":"","title":"Bayesian optimization in high dimensions via random embeddings","year":2013,"lang":"en","type":"article","venue":"UvA-DARE (University of Amsterdam)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":242,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bayesian optimization; Embedding; Computer science; Categorical variable; Solver; Bayesian probability; Artificial intelligence; Optimization problem; Integer programming; Mathematical optimization; Machine learning; Algorithm; 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":[0.0001135647,0.0001685873,0.0002885211,0.0003705941,0.0001898076,0.00004500295,0.0006382253,0.00009800473,0.0003577023],"category_scores_gemma":[0.0000367038,0.0002062514,0.00007846171,0.0007933176,0.0001097244,0.001846663,0.0003784269,0.0001437575,0.00006825211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000134196,"about_ca_system_score_gemma":0.00004801201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006357462,"about_ca_topic_score_gemma":0.00006976183,"domain_scores_codex":[0.9987416,0.0000994376,0.0001873406,0.0004463155,0.0002503914,0.0002749006],"domain_scores_gemma":[0.9988166,0.0001023088,0.0002003873,0.0004441165,0.0003080796,0.000128484],"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":[0.00005650748,0.0002456188,0.001189094,0.00002720327,0.00004392964,0.00005578077,0.004770526,0.9631003,0.0008838414,0.001774185,0.0005716009,0.02728146],"study_design_scores_gemma":[0.002799307,0.00006112404,0.00378117,0.00004401511,0.000008606205,0.000007649332,0.0005630889,0.9914271,0.0001639189,0.0007769536,0.0001242982,0.0002427941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008047475,0.0000170132,0.9894771,0.0007723423,0.0001772768,0.0004326791,0.000005205543,0.000134143,0.0009367754],"genre_scores_gemma":[0.4259001,0.00002087046,0.5734137,0.00009123537,0.00001026032,8.940657e-7,0.00001368672,0.00001085701,0.0005383654],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4178526,"threshold_uncertainty_score":0.8410689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005049378076417137,"score_gpt":0.1907773810526467,"score_spread":0.1857280029762295,"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."}}