{"id":"W1675620121","doi":"10.48550/arxiv.1402.7005","title":"Bayesian Multi-Scale Optimistic Optimization","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bayesian optimization; Regret; Computer science; Mathematical optimization; Global optimization; Optimization problem; Convergence (economics); Gaussian process; Bayesian probability; Test functions for optimization; Derivative-free optimization; Function (biology); Continuous optimization; Random optimization; Gaussian; Algorithm; Multi-swarm optimization; Mathematics; Artificial intelligence; 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":[0.004668181,0.00141071,0.001839193,0.0008274572,0.0008278622,0.002357116,0.00185628,0.001429144,0.004889645],"category_scores_gemma":[0.01223001,0.0009651336,0.001120709,0.001168408,0.001739512,0.002361612,0.003057052,0.003007397,0.001100029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002082041,"about_ca_system_score_gemma":0.002446675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003064139,"about_ca_topic_score_gemma":0.003793041,"domain_scores_codex":[0.997587,0.001040575,0.00009419631,0.0003491839,0.0006604984,0.0002685825],"domain_scores_gemma":[0.9954032,0.003074361,0.000332704,0.0005963479,0.0004285871,0.0001647361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000131222,0.00003531847,0.0003880379,0.0001090585,0.00005853738,0.00004399555,0.00006759183,0.8506765,0.0007775985,0.1051393,0.003231887,0.03934089],"study_design_scores_gemma":[0.00000726661,0.00001290081,0.00005145906,0.00001101394,0.000006925296,0.000009889208,0.000007166557,0.970004,0.0003542507,0.0288736,0.0006553138,0.000006386749],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005954545,0.0003564241,0.9877844,0.0003603591,0.00002860172,0.00003453222,0.00006245415,0.0003878042,0.005030935],"genre_scores_gemma":[0.5788509,0.0007865237,0.409456,0.0004941901,0.0001282057,0.0003226431,0.0003628276,0.0005076286,0.009091083],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004889645,"threshold_uncertainty_score":0.02468795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2206405619657407,"score_gpt":0.3089465051702611,"score_spread":0.08830594320452045,"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."}}