{"id":"https://openalex.org/W4308216454","doi":"https://doi.org/10.1109/allerton49937.2022.9929361","title":"On Sample Complexity of Learning Shared Representations: The Asymptotic Regime","display_name":"On Sample Complexity of Learning Shared Representations: The Asymptotic Regime","publication_year":2022,"publication_date":"2022-09-27","ids":{"openalex":"https://openalex.org/W4308216454","doi":"https://doi.org/10.1109/allerton49937.2022.9929361"},"language":"en","primary_location":{"id":"doi:10.1109/allerton49937.2022.9929361","is_oa":false,"landing_page_url":"https://doi.org/10.1109/allerton49937.2022.9929361","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5061406372","display_name":"Xinyi Tong","orcid":"https://orcid.org/0000-0001-9832-7973"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xinyi Tong","raw_affiliation_strings":["Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University,Shenzhen,China,518055"],"affiliations":[{"raw_affiliation_string":"Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University,Shenzhen,China,518055","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054043281","display_name":"Xiangxiang Xu","orcid":"https://orcid.org/0000-0002-4178-0934"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiangxiang Xu","raw_affiliation_strings":["EECS, MIT,Cambridge,MA,USA,02139"],"affiliations":[{"raw_affiliation_string":"EECS, MIT,Cambridge,MA,USA,02139","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088293566","display_name":"Shao\u2010Lun Huang","orcid":"https://orcid.org/0000-0003-2827-4022"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]},{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shao-Lun Huang","raw_affiliation_strings":["Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University,Shenzhen,China,518055"],"affiliations":[{"raw_affiliation_string":"Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University,Shenzhen,China,518055","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5061406372"],"corresponding_institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.1326,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.53761153,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9995999932289124,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9995999932289124,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.995199978351593,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9944000244140625,"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/computer-science","display_name":"Computer science","score":0.7800977826118469},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.7069593667984009},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.6385040283203125},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.610033392906189},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.608532726764679},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6015748381614685},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5984141826629639},{"id":"https://openalex.org/keywords/sample-complexity","display_name":"Sample complexity","score":0.5760261416435242},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5359148979187012},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4985508918762207},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4685543477535248},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4632369875907898},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45831793546676636},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4296523332595825},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.42137137055397034},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11306881904602051},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08174926042556763}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7800977826118469},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.7069593667984009},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6385040283203125},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.610033392906189},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.608532726764679},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6015748381614685},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5984141826629639},{"id":"https://openalex.org/C2778445095","wikidata":"https://www.wikidata.org/wiki/Q18354077","display_name":"Sample complexity","level":2,"score":0.5760261416435242},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5359148979187012},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4985508918762207},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4685543477535248},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4632369875907898},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45831793546676636},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4296523332595825},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.42137137055397034},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11306881904602051},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08174926042556763},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"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/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/allerton49937.2022.9929361","is_oa":false,"landing_page_url":"https://doi.org/10.1109/allerton49937.2022.9929361","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3043234225","display_name":null,"funder_award_id":"2021YFA0715202","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W83404773","https://openalex.org/W2154518298","https://openalex.org/W2163922914","https://openalex.org/W2478708596","https://openalex.org/W2751862591","https://openalex.org/W2900594532","https://openalex.org/W2913340405","https://openalex.org/W2976504920","https://openalex.org/W2991489140","https://openalex.org/W3021654819","https://openalex.org/W3141797743","https://openalex.org/W3196965143","https://openalex.org/W3211115474","https://openalex.org/W4229706427","https://openalex.org/W4246219036","https://openalex.org/W4287906413","https://openalex.org/W4297785590","https://openalex.org/W4318619660","https://openalex.org/W4361192893","https://openalex.org/W6728757088","https://openalex.org/W6770506162","https://openalex.org/W6772318479","https://openalex.org/W6791102956","https://openalex.org/W6791182590","https://openalex.org/W6803488584"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W4312814274","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2353836703"],"abstract_inverted_index":{"Federated":[0],"learning":[1,4,44,102],"and":[2,93,101,135],"multi-task":[3],"have":[5,171],"achieved":[6],"great":[7],"successes":[8],"in":[9,17,156],"recent":[10],"works,":[11],"which":[12,139,169],"exploit":[13],"the":[14,22,32,38,42,53,59,89,97,109,119,122,129,132,136,141,160,166,179],"knowledge":[15,33],"contained":[16],"different":[18,106,163],"tasks":[19,62],"to":[20,30],"alleviate":[21],"influence":[23],"of":[24,41,61,91,105,121,158,162,182],"lacking":[25],"training":[26,74,83,96],"samples.":[27],"In":[28],"order":[29],"understand":[31],"utilization":[34],"mechanism,":[35],"we":[36,66,151],"study":[37],"sample":[39,137,142,173,184],"complexity":[40,174],"federated":[43],"algorithms":[45],"by":[46,78,87,128],"computing":[47],"their":[48],"expected":[49],"population":[50],"risks,":[51],"where":[52,108,145],"asymptotic":[54,180],"regime":[55,181],"is":[56,63,112,126,148],"considered":[57],"that":[58,118],"number":[60],"large.":[64],"Specifically,":[65],"focus":[67],"on":[68],"several":[69],"typical":[70],"algorithms,":[71],"including":[72],"(i)":[73],"personalized":[75],"models":[76],"merely":[77],"local":[79],"data,":[80],"(ii)":[81],"jointly":[82,95],"a":[84,153],"single":[85],"model":[86,133],"all":[88],"data":[90],"devices,":[92,107],"(iii)":[94],"shared":[98,123,167],"feature":[99],"representation":[100,124],"individual":[103],"classifiers":[104,161],"latter":[110],"one":[111],"recently":[113],"widely-adopted.":[114],"The":[115],"results":[116],"reveal":[117],"validity":[120],"algorithm":[125,147,155,192],"affected":[127],"task":[130],"similarities,":[131],"dimensionality,":[134],"size,":[138],"quantify":[140],"size":[143],"range":[144],"such":[146],"preferable.":[149],"Moreover,":[150],"develop":[152],"new":[154],"consideration":[157],"collaborating":[159],"devices":[164],"under":[165,178],"representations,":[168],"can":[170],"lower":[172],"than":[175],"conventional":[176],"methods":[177],"large":[183],"size.":[185],"This":[186],"provides":[187],"theoretical":[188],"guidance":[189],"for":[190],"practical":[191],"designs.":[193]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}