{"id":"https://openalex.org/W4280539728","doi":"https://doi.org/10.1109/tnnls.2022.3171171","title":"Fast Rates of Gaussian Empirical Gain Maximization With Heavy-Tailed Noise","display_name":"Fast Rates of Gaussian Empirical Gain Maximization With Heavy-Tailed Noise","publication_year":2022,"publication_date":"2022-05-13","ids":{"openalex":"https://openalex.org/W4280539728","doi":"https://doi.org/10.1109/tnnls.2022.3171171","pmid":"https://pubmed.ncbi.nlm.nih.gov/35560074"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2022.3171171","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3171171","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103030441","display_name":"Shouyou Huang","orcid":"https://orcid.org/0000-0003-2705-5370"},"institutions":[{"id":"https://openalex.org/I4210165606","display_name":"Hubei Normal University","ror":"https://ror.org/056y3dw16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210165606"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shouyou Huang","raw_affiliation_strings":["Department of Mathematics and Statistics, Hubei Normal University, Huangshi, China"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, Hubei Normal University, Huangshi, China","institution_ids":["https://openalex.org/I4210165606"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101673055","display_name":"Yunlong Feng","orcid":"https://orcid.org/0000-0002-1519-2717"},"institutions":[{"id":"https://openalex.org/I392282","display_name":"University at Albany, State University of New York","ror":"https://ror.org/012zs8222","country_code":"US","type":"education","lineage":["https://openalex.org/I392282"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yunlong Feng","raw_affiliation_strings":["Department of Mathematics and Statistics, University at Albany, Albany, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, University at Albany, Albany, NY, USA","institution_ids":["https://openalex.org/I392282"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089720861","display_name":"Qiang Wu","orcid":"https://orcid.org/0000-0002-4698-6966"},"institutions":[{"id":"https://openalex.org/I169615421","display_name":"Middle Tennessee State University","ror":"https://ror.org/02n1hzn07","country_code":"US","type":"education","lineage":["https://openalex.org/I169615421"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qiang Wu","raw_affiliation_strings":["Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, TN, USA"],"affiliations":[{"raw_affiliation_string":"Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, TN, USA","institution_ids":["https://openalex.org/I169615421"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103030441"],"corresponding_institution_ids":["https://openalex.org/I4210165606"],"apc_list":null,"apc_paid":null,"fwci":1.7848,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.84367099,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"33","issue":"10","first_page":"6038","last_page":"6043"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11236","display_name":"Control Systems and Identification","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.653192400932312},{"id":"https://openalex.org/keywords/moment","display_name":"Moment (physics)","score":0.6290709972381592},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5644940137863159},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5547072291374207},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.5455405712127686},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.5114273428916931},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5110024213790894},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.5072504878044128},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.47325924038887024},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.46803098917007446},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.46112507581710815},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4494186043739319},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.39929434657096863},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.31587961316108704},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.26986849308013916},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25581398606300354},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07867735624313354}],"concepts":[{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.653192400932312},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.6290709972381592},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5644940137863159},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5547072291374207},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.5455405712127686},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.5114273428916931},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5110024213790894},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5072504878044128},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.47325924038887024},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.46803098917007446},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.46112507581710815},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4494186043739319},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.39929434657096863},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.31587961316108704},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26986849308013916},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25581398606300354},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07867735624313354},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2022.3171171","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3171171","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:35560074","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35560074","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.41999998688697815}],"awards":[{"id":"https://openalex.org/G21294879","display_name":null,"funder_award_id":"12126320","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3348347374","display_name":null,"funder_award_id":"712916","funder_id":"https://openalex.org/F4320306164","funder_display_name":"Simons Foundation"},{"id":"https://openalex.org/G5165783572","display_name":null,"funder_award_id":"12126365","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8211968369","display_name":null,"funder_award_id":"572064","funder_id":"https://openalex.org/F4320306164","funder_display_name":"Simons Foundation"}],"funders":[{"id":"https://openalex.org/F4320306164","display_name":"Simons Foundation","ror":"https://ror.org/01cmst727"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1496317909","https://openalex.org/W1531818640","https://openalex.org/W1903303072","https://openalex.org/W2046033161","https://openalex.org/W2069627663","https://openalex.org/W2108840547","https://openalex.org/W2135160607","https://openalex.org/W2137823674","https://openalex.org/W2141767566","https://openalex.org/W2147184730","https://openalex.org/W2148665023","https://openalex.org/W2156909104","https://openalex.org/W2166329520","https://openalex.org/W2171616083","https://openalex.org/W2345904635","https://openalex.org/W2963134661","https://openalex.org/W2989253080","https://openalex.org/W3007928625","https://openalex.org/W3033779185","https://openalex.org/W3099761860","https://openalex.org/W3127795058","https://openalex.org/W3152696736","https://openalex.org/W3154298967","https://openalex.org/W3187217851","https://openalex.org/W4245558064","https://openalex.org/W6639612093","https://openalex.org/W6682142706","https://openalex.org/W6741411919","https://openalex.org/W6875288825"],"related_works":["https://openalex.org/W2947806671","https://openalex.org/W2390878257","https://openalex.org/W2144260821","https://openalex.org/W2088123951","https://openalex.org/W2141163797","https://openalex.org/W2118922860","https://openalex.org/W4313429348","https://openalex.org/W1970319972","https://openalex.org/W2953254336","https://openalex.org/W2112030392"],"abstract_inverted_index":{"In":[0,27],"a":[1,19,31,62,87],"regression":[2,41,98],"setup,":[3],"we":[4,29,59,84],"study":[5,118],"in":[6,49,110],"this":[7],"brief":[8],"the":[9,50,70,73,81,97,116],"performance":[10],"of":[11,22,52,101,123],"Gaussian":[12,37,102,124],"empirical":[13],"gain":[14],"maximization":[15],"(EGM),":[16],"which":[17],"includes":[18],"broad":[20],"variety":[21],"well-established":[23],"robust":[24],"estimation":[25],"approaches.":[26],"particular,":[28],"conduct":[30],"refined":[32],"learning":[33],"theory":[34],"analysis":[35],"for":[36],"EGM,":[38],"investigate":[39],"its":[40],"calibration":[42,99],"properties,":[43],"and":[44],"develop":[45,86],"improved":[46,112],"convergence":[47,113],"rates":[48],"presence":[51],"heavy-tailed":[53],"noise.":[54],"To":[55],"achieve":[56],"these":[57],"purposes,":[58],"first":[60],"introduce":[61],"new":[63],"weak":[64],"moment":[65,82],"condition":[66],"that":[67,91],"could":[68],"accommodate":[69],"cases":[71],"where":[72],"noise":[74],"distribution":[75],"may":[76],"be":[77,93],"heavy-tailed.":[78],"Based":[79],"on":[80],"condition,":[83],"then":[85],"novel":[88],"comparison":[89],"theorem":[90],"can":[92],"used":[94],"to":[95],"characterize":[96],"properties":[100],"EGM.":[103,125],"It":[104],"also":[105],"plays":[106],"an":[107],"essential":[108],"role":[109],"deriving":[111],"rates.":[114],"Therefore,":[115],"present":[117],"broadens":[119],"our":[120],"theoretical":[121],"understanding":[122]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
