{"id":"W2988771853","doi":"10.48550/arxiv.1911.01012","title":"StoMADS: Stochastic blackbox optimization using probabilistic estimates","year":2019,"lang":"en","type":"preprint","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Polytechnique Montréal","funders":"","keywords":"Mathematical optimization; Probabilistic logic; Martingale (probability theory); Convergence (economics); Computer science; Mathematics; Probability distribution; Stochastic optimization; Set (abstract data type); Algorithm; Convergence of random variables; Random variable; Applied mathematics; Artificial intelligence","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":[0.001712604,0.0007796052,0.001265527,0.0006349633,0.0003626874,0.001142761,0.001168209,0.001099308,0.002602581],"category_scores_gemma":[0.004863783,0.0006469215,0.0008605067,0.0005013797,0.001136629,0.00110714,0.00203317,0.001420321,0.0004345437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007134693,"about_ca_system_score_gemma":0.001060256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001667681,"about_ca_topic_score_gemma":0.001337248,"domain_scores_codex":[0.9993112,0.0003161555,0.00002467041,0.00009952404,0.0002111373,0.00003737202],"domain_scores_gemma":[0.9982943,0.001299224,0.0001192889,0.000104285,0.0001243606,0.00005864561],"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":[0.00004733599,0.00001530307,0.0002178833,0.00006574678,0.00003036286,0.00003034717,0.00002406263,0.9442886,0.0009664068,0.03916166,0.0005066412,0.01464571],"study_design_scores_gemma":[0.000005956797,0.000008530701,0.00001564956,0.00000493328,0.000001626713,0.000003697588,0.000001393275,0.9919282,0.0002122316,0.007452232,0.0003632487,0.000002250763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002980831,0.00009671237,0.9958388,0.00006430463,0.00002097092,0.0000184819,0.00002064519,0.0001263605,0.0008330059],"genre_scores_gemma":[0.4100381,0.0004105793,0.5835006,0.0002054234,0.00008256408,0.0003896436,0.0002447559,0.0003367092,0.004791542],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002602581,"threshold_uncertainty_score":0.009057224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02513923476694892,"score_gpt":0.2802149912575571,"score_spread":0.2550757564906082,"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."}}