{"id":"W4401814194","doi":"10.1090/mcom/4011","title":"Expected decrease for derivative-free algorithms using random subspaces","year":2024,"lang":"en","type":"article","venue":"Mathematics of Computation","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Linear subspace; Mathematics; Leverage (statistics); Dimension (graph theory); Subspace topology; Algorithm; Mathematical optimization; Computation; Derivative (finance); Function (biology); Pure mathematics; Statistics; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"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":[0.008159072,0.001243132,0.001273612,0.001156167,0.0007947614,0.002300945,0.002047484,0.001573643,0.003582709],"category_scores_gemma":[0.06138749,0.0005108699,0.00106762,0.0009331212,0.002714483,0.00463426,0.002157963,0.002783141,0.0008920507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002421717,"about_ca_system_score_gemma":0.002082867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009191993,"about_ca_topic_score_gemma":0.0009992094,"domain_scores_codex":[0.9949333,0.002134279,0.0002210739,0.0006067207,0.001597692,0.0005068652],"domain_scores_gemma":[0.9484633,0.04216487,0.001672034,0.003748432,0.003118148,0.0008332097],"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":[0.000743978,0.0002204406,0.00352512,0.0004929467,0.0001322911,0.0001157259,0.0001806364,0.8093181,0.006313924,0.1361575,0.002971073,0.03982826],"study_design_scores_gemma":[0.00001783788,0.000145851,0.0004716415,0.00003663253,0.00001729426,0.00005650031,0.0000215984,0.9666992,0.002066593,0.02993857,0.0005136707,0.00001459673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07125575,0.001683122,0.9184281,0.001118101,0.00008518268,0.0001139194,0.0001708544,0.0006551542,0.006489807],"genre_scores_gemma":[0.7874347,0.001254821,0.2036937,0.000590108,0.0001425563,0.0004601718,0.0005911941,0.0007286056,0.005104121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008159072,"threshold_uncertainty_score":0.04314983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02951130113500457,"score_gpt":0.3173608825810777,"score_spread":0.2878495814460731,"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."}}