{"id":"https://openalex.org/W2976518359","doi":"https://doi.org/10.1109/isit.2019.8849669","title":"Minimax Regression via Adaptive Nearest Neighbor","display_name":"Minimax Regression via Adaptive Nearest Neighbor","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2976518359","doi":"https://doi.org/10.1109/isit.2019.8849669","mag":"2976518359"},"language":"en","primary_location":{"id":"doi:10.1109/isit.2019.8849669","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2019.8849669","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Symposium on Information Theory (ISIT)","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/A5035733773","display_name":"Puning Zhao","orcid":"https://orcid.org/0009-0002-3264-3417"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Puning Zhao","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of California, Davis","Department of Electrical and Computer Engineering, University of California-Davis"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California, Davis","institution_ids":["https://openalex.org/I84218800"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California-Davis","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081715462","display_name":"Lifeng Lai","orcid":"https://orcid.org/0000-0002-9493-8248"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lifeng Lai","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of California, Davis","Department of Electrical and Computer Engineering, University of California-Davis"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California, Davis","institution_ids":["https://openalex.org/I84218800"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California-Davis","institution_ids":["https://openalex.org/I84218800"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5035733773"],"corresponding_institution_ids":["https://openalex.org/I84218800"],"apc_list":null,"apc_paid":null,"fwci":0.9599,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.78058087,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1447","last_page":"1451"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9909999966621399,"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"}},"topics":[{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9909999966621399,"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/T10057","display_name":"Face and Expression Recognition","score":0.989799976348877,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9855999946594238,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/minimax","display_name":"Minimax","score":0.7609891891479492},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.6234294772148132},{"id":"https://openalex.org/keywords/smoothness","display_name":"Smoothness","score":0.5853803157806396},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.5806084275245667},{"id":"https://openalex.org/keywords/nonparametric-regression","display_name":"Nonparametric regression","score":0.5524781346321106},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5283862948417664},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4871947169303894},{"id":"https://openalex.org/keywords/bounded-function","display_name":"Bounded function","score":0.44407618045806885},{"id":"https://openalex.org/keywords/rate-of-convergence","display_name":"Rate of convergence","score":0.4406302571296692},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4352279305458069},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.367454469203949},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3320502042770386},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.30878347158432007},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2479669451713562}],"concepts":[{"id":"https://openalex.org/C149728462","wikidata":"https://www.wikidata.org/wiki/Q751319","display_name":"Minimax","level":2,"score":0.7609891891479492},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.6234294772148132},{"id":"https://openalex.org/C102634674","wikidata":"https://www.wikidata.org/wiki/Q868473","display_name":"Smoothness","level":2,"score":0.5853803157806396},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.5806084275245667},{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.5524781346321106},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5283862948417664},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4871947169303894},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.44407618045806885},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.4406302571296692},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4352279305458069},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.367454469203949},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3320502042770386},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.30878347158432007},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2479669451713562},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isit.2019.8849669","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2019.8849669","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4300000071525574,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1484867920","https://openalex.org/W1511694993","https://openalex.org/W1571975558","https://openalex.org/W1990707244","https://openalex.org/W2012227947","https://openalex.org/W2051218140","https://openalex.org/W2059507684","https://openalex.org/W2092149755","https://openalex.org/W2116464185","https://openalex.org/W2122111042","https://openalex.org/W2303810353","https://openalex.org/W2326104347","https://openalex.org/W2787929469","https://openalex.org/W2884334374","https://openalex.org/W2911964244","https://openalex.org/W6748408121"],"related_works":["https://openalex.org/W2016058626","https://openalex.org/W2474724840","https://openalex.org/W2895916002","https://openalex.org/W1814049089","https://openalex.org/W2393022482","https://openalex.org/W1977348009","https://openalex.org/W2377346130","https://openalex.org/W2369683208","https://openalex.org/W4385964707","https://openalex.org/W4214665796"],"abstract_inverted_index":{"In":[0],"this":[1,48,73,120,188],"paper,":[2],"we":[3,75,105,123,133],"investigate":[4],"the":[5,18,36,57,62,86,91,100,112,136,145,155,164,167,177,182],"convergence":[6,137],"rate":[7,138,152],"of":[8,38,114,139],"k":[9,64,84,115],"Nearest":[10],"Neighbor":[11],"(kNN)":[12],"regression":[13,23,142,157,172,185],"methods.":[14],"We":[15,161],"first":[16],"derive":[17],"minimax":[19,49,146],"bound":[20,32,50,148],"for":[21,67],"nonparametric":[22],"under":[24],"some":[25],"general":[26],"tail":[27],"and":[28,51,96,130,149,174],"smoothness":[29],"assumptions.":[30],"This":[31],"shows":[33],"that,":[34],"when":[35,85,154],"distribution":[37],"features":[39],"has":[40],"heavy":[41],"tails,":[42],"there":[43],"is":[44,65,103,151,159],"a":[45,107,125],"gap":[46],"between":[47,128],"that":[52,81,135,176],"can":[53],"be":[54],"achieved":[55],"by":[56],"standard":[58,183],"kNN":[59,79,184],"methods":[60],"where":[61],"same":[63],"used":[66],"all":[68],"query":[69,87],"points.":[70],"To":[71],"close":[72],"gap,":[74],"propose":[76],"an":[77],"adaptive":[78],"method":[80,109,143,179,186],"selects":[82],"smaller":[83],"sample":[88],"falls":[89],"in":[90,187],"region":[92],"with":[93,169],"lower":[94,147],"density,":[95],"vice":[97],"versa.":[98],"As":[99],"density":[101],"function":[102,158],"unknown,":[104],"design":[106],"simple":[108],"to":[110,166],"determine":[111],"value":[113],"from":[116],"training":[117],"samples.":[118],"Using":[119],"selection":[121],"rule,":[122],"obtain":[124],"desirable":[126],"tradeoff":[127],"bias":[129],"variance.":[131],"Furthermore,":[132],"show":[134,175],"our":[140],"new":[141],"attains":[144],"hence":[150],"optimal":[153],"underlying":[156,171],"bounded.":[160],"further":[162],"extend":[163],"analysis":[165],"case":[168,189],"unbounded":[170],"functions":[173],"proposed":[178],"significantly":[180],"outperforms":[181],"as":[190],"well.":[191]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-03-14T06:41:57.775601","created_date":"2025-10-10T00:00:00"}
