{"id":"https://openalex.org/W2905119692","doi":"https://doi.org/10.1609/aaai.v33i01.33014229","title":"Learning Adaptive Random Features","display_name":"Learning Adaptive Random Features","publication_year":2019,"publication_date":"2019-07-17","ids":{"openalex":"https://openalex.org/W2905119692","doi":"https://doi.org/10.1609/aaai.v33i01.33014229","mag":"2905119692"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v33i01.33014229","doi":"https://doi.org/10.1609/aaai.v33i01.33014229","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33014229","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v33i01.33014229","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100382131","display_name":"Yanjun Li","orcid":"https://orcid.org/0000-0003-2814-5765","observed_orcids":["https://orcid.org/0000-0003-2814-5765"]},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanjun Li","raw_affiliation_strings":["University of Illinois, Urbana Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois, Urbana Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107559430","display_name":"Kai Zhang","orcid":"https://orcid.org/0000-0002-9692-4333","observed_orcids":["https://orcid.org/0000-0002-9692-4333","https://orcid.org/0009-0004-1688-1815","https://orcid.org/0000-0001-6297-4423","https://orcid.org/0000-0002-1188-2120","https://orcid.org/0000-0002-6691-6762","https://orcid.org/0000-0003-3850-5429","https://orcid.org/0000-0002-2984-5985"]},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kai Zhang","raw_affiliation_strings":["Temple University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Temple University","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jun Wang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Wang","raw_affiliation_strings":["Alibaba"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alibaba","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018301052","display_name":"Sanjiv Kumar","orcid":"https://orcid.org/0000-0002-4080-1414","observed_orcids":["https://orcid.org/0000-0002-4080-1414"]},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sanjiv Kumar","raw_affiliation_strings":["Google Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Research","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4877,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.67173327,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":97},"biblio":{"volume":"33","issue":"01","first_page":"4229","last_page":"4236"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.8833000063896179,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.8833000063896179,"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/T12676","display_name":"Machine Learning and ELM","score":0.08079999685287476,"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/T10320","display_name":"Neural Networks and Applications","score":0.019600000232458115,"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/random-fourier-features","display_name":"random Fourier features","score":0.9430000185966492},{"id":"https://openalex.org/keywords/kernel-approximation","display_name":"kernel approximation","score":0.8240000009536743},{"id":"https://openalex.org/keywords/shift-invariant-kernels","display_name":"shift-invariant kernels","score":0.8050000071525574},{"id":"https://openalex.org/keywords/feature-weights","display_name":"feature weights","score":0.6769999861717224},{"id":"https://openalex.org/keywords/rademacher-complexity","display_name":"Rademacher complexity","score":0.8569999933242798},{"id":"https://openalex.org/keywords/generalization-bounds","display_name":"generalization bounds","score":0.9990000128746033},{"id":"https://openalex.org/keywords/regularization","display_name":"regularization","score":0.621999979019165},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"supervised learning","score":0.6669999957084656},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"dimensionality reduction","score":0.7519999742507935}],"concepts":[{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5680428147315979},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5573208928108215},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5568962097167969},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4716472029685974},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.46558135747909546},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.4364430606365204},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.43177932500839233},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4113262891769409},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.39311790466308594},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37981027364730835},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33512407541275024},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.1887577772140503},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"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":2,"locations":[{"id":"doi:10.1609/aaai.v33i01.33014229","doi":"https://doi.org/10.1609/aaai.v33i01.33014229","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33014229","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:ojs.aaai.org:article/4329","doi":null,"is_oa":true,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/4329","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/4329/4207","source":{"id":"https://openalex.org/S4306400155","display_name":"AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI))","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801652627","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/I2801652627"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v33i01.33014229","doi":"https://doi.org/10.1609/aaai.v33i01.33014229","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33014229","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G8988487328","display_name":null,"funder_award_id":"61672236","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2905119692.pdf","grobid_xml":"https://content.openalex.org/works/W2905119692.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1590183771","https://openalex.org/W1814624729","https://openalex.org/W1999352252","https://openalex.org/W2020733616","https://openalex.org/W2054098737","https://openalex.org/W2109235804","https://openalex.org/W2109844857","https://openalex.org/W2116810533","https://openalex.org/W2117756735","https://openalex.org/W2118563516","https://openalex.org/W2144902422","https://openalex.org/W2147118250","https://openalex.org/W2151744781","https://openalex.org/W2157988812","https://openalex.org/W2171792531","https://openalex.org/W2182347905","https://openalex.org/W2224709232","https://openalex.org/W2322150470","https://openalex.org/W2549898883","https://openalex.org/W2604310683","https://openalex.org/W2616566795","https://openalex.org/W2962910688","https://openalex.org/W2963477567","https://openalex.org/W2989661724","https://openalex.org/W3144619878","https://openalex.org/W3195149063","https://openalex.org/W4244670803","https://openalex.org/W6676101543"],"related_works":["https://openalex.org/W4283802612","https://openalex.org/W2963459001","https://openalex.org/W2997839189","https://openalex.org/W2549898883","https://openalex.org/W2171155677","https://openalex.org/W2511680389","https://openalex.org/W2885554660","https://openalex.org/W2963477567","https://openalex.org/W2886452169","https://openalex.org/W2804128720"],"abstract_inverted_index":{"Random":[0],"Fourier":[1,62,89],"features":[2,63,90],"are":[3],"a":[4,74],"powerful":[5],"framework":[6],"to":[7,40,56,64,84],"approximate":[8],"shift":[9],"invariant":[10],"kernels":[11],"with":[12,91,121],"Monte":[13],"Carlo":[14],"integration,":[15],"which":[16],"has":[17],"drawn":[18],"considerable":[19],"interest":[20],"in":[21],"scaling":[22],"up":[23],"kernel-based":[24],"learning,":[25],"dimensionality":[26],"reduction,":[27],"and":[28,49,96,133],"information":[29],"retrieval.":[30],"In":[31,79],"the":[32,42,58,107,144],"literature,":[33],"many":[34],"sampling":[35,77,109],"schemes":[36],"have":[37],"been":[38],"proposed":[39],"improve":[41],"approximation":[43,68,147],"performance.":[44],"However,":[45],"an":[46],"interesting":[47],"theoretic":[48],"algorithmic":[50],"challenge":[51],"still":[52],"remains,":[53],"i.e.,":[54],"how":[55],"optimize":[57],"design":[59],"of":[60],"random":[61,88],"achieve":[65],"good":[66],"kernel":[67,114,146],"on":[69],"any":[70,122],"input":[71],"data":[72],"using":[73,130],"low":[75],"spectral":[76,93,108],"rate?":[78],"this":[80],"paper,":[81],"we":[82],"propose":[83],"compute":[85],"more":[86],"adaptive":[87],"optimized":[92],"samples":[94],"(wj\u2019s)":[95],"feature":[97],"weights":[98],"(pj\u2019s).":[99],"The":[100],"learning":[101,124],"scheme":[102],"not":[103],"only":[104],"significantly":[105],"reduces":[106],"rate":[110],"needed":[111],"for":[112,151],"accurate":[113],"approximation,":[115],"but":[116],"also":[117],"allows":[118],"joint":[119],"optimization":[120],"supervised":[123,152],"framework.":[125],"We":[126],"establish":[127],"generalization":[128],"bounds":[129],"Rademacher":[131],"complexity,":[132],"demonstrate":[134],"advantages":[135],"over":[136],"previous":[137],"methods.":[138],"Moreover,":[139],"our":[140],"experiments":[141],"show":[142],"that":[143],"empirical":[145],"provides":[148],"effective":[149],"regularization":[150],"learning.":[153]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2018-12-22T00:00:00"}
