{"id":"https://openalex.org/W2964996052","doi":"https://doi.org/10.1137/20m1343300","title":"Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting","display_name":"Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W2964996052","doi":"https://doi.org/10.1137/20m1343300","mag":"2964996052"},"language":"en","primary_location":{"id":"doi:10.1137/20m1343300","is_oa":true,"landing_page_url":"https://doi.org/10.1137/20m1343300","pdf_url":null,"source":{"id":"https://openalex.org/S4210229561","display_name":"SIAM Journal on Mathematics of Data Science","issn_l":"2577-0187","issn":["2577-0187"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Mathematics of Data Science","raw_type":"journal-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1137/20m1343300","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037982747","display_name":"Miles E. Lopes","orcid":"https://orcid.org/0000-0002-8698-7736"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Miles E. Lopes","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110697818","display_name":"Suofei Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Suofei Wu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5049635087","display_name":"Thomas C. M. Lee","orcid":"https://orcid.org/0000-0001-7067-405X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thomas C. M. Lee","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5037982747"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.00645554,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"2","issue":"4","first_page":"921","last_page":"943"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.6376755237579346},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6084579825401306},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6049745082855225},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5946365594863892},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5901104211807251},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5675473809242249},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5364322066307068},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5361644625663757},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.5133303999900818},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5078588128089905},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.48658716678619385},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4718209207057953},{"id":"https://openalex.org/keywords/ideal","display_name":"Ideal (ethics)","score":0.46013277769088745},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4218232333660126},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.34242314100265503},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.22992268204689026},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1755829155445099}],"concepts":[{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.6376755237579346},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6084579825401306},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6049745082855225},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5946365594863892},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5901104211807251},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5675473809242249},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5364322066307068},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5361644625663757},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.5133303999900818},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5078588128089905},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.48658716678619385},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4718209207057953},{"id":"https://openalex.org/C2776639384","wikidata":"https://www.wikidata.org/wiki/Q840396","display_name":"Ideal (ethics)","level":2,"score":0.46013277769088745},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4218232333660126},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34242314100265503},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.22992268204689026},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1755829155445099},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1137/20m1343300","is_oa":true,"landing_page_url":"https://doi.org/10.1137/20m1343300","pdf_url":null,"source":{"id":"https://openalex.org/S4210229561","display_name":"SIAM Journal on Mathematics of Data Science","issn_l":"2577-0187","issn":["2577-0187"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Mathematics of Data Science","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1908.01251","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1908.01251","pdf_url":"https://arxiv.org/pdf/1908.01251","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:2964996052","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/1908.01251","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1908.01251","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1908.01251","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.1137/20m1343300","is_oa":true,"landing_page_url":"https://doi.org/10.1137/20m1343300","pdf_url":null,"source":{"id":"https://openalex.org/S4210229561","display_name":"SIAM Journal on Mathematics of Data Science","issn_l":"2577-0187","issn":["2577-0187"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Mathematics of Data Science","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Life in Land","score":0.6499999761581421,"id":"https://metadata.un.org/sdg/15"}],"awards":[{"id":"https://openalex.org/G1075873505","display_name":null,"funder_award_id":"1811661","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G1382340671","display_name":null,"funder_award_id":"1811405","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3213249797","display_name":null,"funder_award_id":"1915786","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5794116468","display_name":null,"funder_award_id":"1613218","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8010188014","display_name":null,"funder_award_id":"1916125","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1520812622","https://openalex.org/W1532859614","https://openalex.org/W1587026990","https://openalex.org/W1875028359","https://openalex.org/W1875061881","https://openalex.org/W1906343640","https://openalex.org/W1965524806","https://openalex.org/W1971772153","https://openalex.org/W1982011863","https://openalex.org/W1983751776","https://openalex.org/W1988645907","https://openalex.org/W2011876338","https://openalex.org/W2017397941","https://openalex.org/W2025533349","https://openalex.org/W2034926402","https://openalex.org/W2047081748","https://openalex.org/W2070073022","https://openalex.org/W2075288177","https://openalex.org/W2105234758","https://openalex.org/W2115380778","https://openalex.org/W2126292488","https://openalex.org/W2143481518","https://openalex.org/W2148414029","https://openalex.org/W2150555551","https://openalex.org/W2155261478","https://openalex.org/W2162387923","https://openalex.org/W2169178923","https://openalex.org/W2169281690","https://openalex.org/W2220051020","https://openalex.org/W2557117995","https://openalex.org/W2802643674","https://openalex.org/W2908710465","https://openalex.org/W2911964244","https://openalex.org/W2914923420","https://openalex.org/W3029642367","https://openalex.org/W3199104097","https://openalex.org/W4244313837"],"related_works":["https://openalex.org/W2962955680","https://openalex.org/W142683009","https://openalex.org/W3095857158","https://openalex.org/W2131542408","https://openalex.org/W2963979365","https://openalex.org/W3035870802","https://openalex.org/W67950344","https://openalex.org/W3210025999","https://openalex.org/W2794354164","https://openalex.org/W3133989806","https://openalex.org/W3110394586","https://openalex.org/W92292672","https://openalex.org/W3091852560","https://openalex.org/W1913920913","https://openalex.org/W3206488636","https://openalex.org/W1914062059","https://openalex.org/W3081694630","https://openalex.org/W2754897579","https://openalex.org/W2150209433","https://openalex.org/W3005529192"],"abstract_inverted_index":{"When":[0],"randomized":[1],"ensemble":[2,18,32,42],"methods":[3],"such":[4],"as":[5,36,38],"bagging":[6],"and":[7],"random":[8],"forests":[9],"are":[10],"implemented,":[11],"a":[12,26,30,57,141],"basic":[13],"question":[14],"arises:":[15],"Is":[16],"the":[17,23,45,51,65,76,85,88,96,110,113,136],"large":[19],"enough?":[20],"In":[21,82,106],"particular,":[22],"practitioner":[24],"desires":[25],"rigorous":[27],"guarantee":[28],"that":[29,92,135],"given":[31],"will":[33],"perform":[34],"nearly":[35],"well":[37,139],"an":[39],"ideal":[40],"infinite":[41],"(trained":[43],"on":[44],"same":[46],"data).":[47],"The":[48],"purpose":[49],"of":[50,67,78,112,143],"current":[52,89],"paper":[53,74,90],"is":[54],"to":[55,84,122],"develop":[56],"bootstrap":[58,98],"method":[59,114,137],"for":[60,95,126],"solving":[61],"this":[62],"problem":[63],"in":[64,75,140],"context":[66,77],"regression":[68],"---":[69],"which":[70],"complements":[71],"our":[72],"companion":[73],"classification":[79,86],"(Lopes":[80],"2019).":[81],"contrast":[83],"setting,":[87],"shows":[91],"theoretical":[93],"guarantees":[94],"proposed":[97],"can":[99,119],"be":[100,120],"established":[101],"under":[102],"much":[103],"weaker":[104],"assumptions.":[105],"addition,":[107],"we":[108,130],"illustrate":[109],"flexibility":[111],"by":[115],"showing":[116],"how":[117],"it":[118],"adapted":[121],"measure":[123],"algorithmic":[124],"convergence":[125],"variable":[127],"selection.":[128],"Lastly,":[129],"provide":[131],"numerical":[132],"results":[133],"demonstrating":[134],"works":[138],"range":[142],"situations.":[144]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
