{"id":"https://openalex.org/W2802686027","doi":"https://doi.org/10.1145/3184066.3184097","title":"Research on hyper parameter tuning for distributed optimization when data is sparse","display_name":"Research on hyper parameter tuning for distributed optimization when data is sparse","publication_year":2018,"publication_date":"2018-02-02","ids":{"openalex":"https://openalex.org/W2802686027","doi":"https://doi.org/10.1145/3184066.3184097","mag":"2802686027"},"language":"en","primary_location":{"id":"doi:10.1145/3184066.3184097","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3184066.3184097","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Machine Learning and Soft Computing","raw_type":"proceedings-article"},"type":"conference-paper","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/A5104076757","display_name":"Fang Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang Xiao","raw_affiliation_strings":["CFETS Information Technology (Shanghai) Co., Ltd, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CFETS Information Technology (Shanghai) Co., Ltd, Shanghai, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040978734","display_name":"Bao Maomao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bao Maomao","raw_affiliation_strings":["CFETS Information Technology (Shanghai) Co., Ltd, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CFETS Information Technology (Shanghai) Co., Ltd, Shanghai, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105367031","display_name":"Chen Yu-ting","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen Yuting","raw_affiliation_strings":["CFETS Information Technology (Shanghai) Co., Ltd, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CFETS Information Technology (Shanghai) Co., Ltd, Shanghai, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110792711","display_name":"Ting Mao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mao Ting","raw_affiliation_strings":["CFETS Information Technology (Shanghai) Co., Ltd, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CFETS Information Technology (Shanghai) Co., Ltd, Shanghai, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"72","last_page":"76"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","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/T12676","display_name":"Machine Learning and ELM","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/T10320","display_name":"Neural Networks and Applications","score":0.991599977016449,"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/T10057","display_name":"Face and Expression Recognition","score":0.9879999756813049,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/asynchronous-communication","display_name":"Asynchronous communication","score":0.8712062835693359},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.779511034488678},{"id":"https://openalex.org/keywords/regret","display_name":"Regret","score":0.667248010635376},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.6442185640335083},{"id":"https://openalex.org/keywords/rate-of-convergence","display_name":"Rate of convergence","score":0.6349158883094788},{"id":"https://openalex.org/keywords/synchronization","display_name":"Synchronization (alternating current)","score":0.629668116569519},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.6033257842063904},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.5444080829620361},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.43755078315734863},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32839566469192505},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23765644431114197},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.17062580585479736},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.07019570469856262}],"concepts":[{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.8712062835693359},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.779511034488678},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.667248010635376},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.6442185640335083},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.6349158883094788},{"id":"https://openalex.org/C2778562939","wikidata":"https://www.wikidata.org/wiki/Q1298791","display_name":"Synchronization (alternating current)","level":3,"score":0.629668116569519},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.6033257842063904},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.5444080829620361},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.43755078315734863},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32839566469192505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23765644431114197},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.17062580585479736},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.07019570469856262},{"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/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/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3184066.3184097","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3184066.3184097","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Machine Learning and Soft Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1594567720","https://openalex.org/W1788809966","https://openalex.org/W2060393849","https://openalex.org/W2074694452","https://openalex.org/W2083842231","https://openalex.org/W2091825929","https://openalex.org/W2106709023","https://openalex.org/W2113651538","https://openalex.org/W2133941677","https://openalex.org/W2138243089","https://openalex.org/W2168231600","https://openalex.org/W2951781666"],"related_works":["https://openalex.org/W4206903459","https://openalex.org/W4283028824","https://openalex.org/W2754816816","https://openalex.org/W4366280654","https://openalex.org/W3160167280","https://openalex.org/W4231621013","https://openalex.org/W4362706668","https://openalex.org/W3008318776","https://openalex.org/W2041416246","https://openalex.org/W3020853991"],"abstract_inverted_index":{"The":[0],"inherent":[1],"sequential":[2],"updating":[3],"mechanism":[4],"of":[5,66,99,110],"gradient":[6,21,68],"descent":[7],"is":[8,35,62,76],"not":[9],"conducive":[10],"to":[11,78],"asynchronous":[12,81,97],"parallelism,":[13],"because":[14],"this":[15,90],"way":[16],"would":[17,43],"induce":[18,44],"much":[19],"staleness":[20,67],"which":[22,48],"could":[23],"slow":[24],"down":[25],"model":[26,34,82,117],"convergence":[27,118],"rate.":[28,119],"Thus,":[29],"BSP":[30],"(bulk":[31],"synchronous":[32],"parallel)":[33],"generally":[36],"adopted":[37],"for":[38,95],"distributed":[39,96],"training.":[40],"However,":[41],"it":[42,75],"extra":[45],"synchronization":[46],"overhead":[47],"will":[49],"greatly":[50],"increase":[51],"training":[52,98],"time.":[53],"Some":[54],"researchers":[55],"pointed":[56],"out":[57],"that":[58],"when":[59],"the":[60,64,108],"data":[61],"sparse,":[63],"probability":[65],"occurred":[69],"can":[70],"be":[71],"reduced":[72],"obviously,":[73],"and":[74,106,113],"feasible":[77],"use":[79],"fully":[80],"in":[83],"engineering.":[84],"Based":[85],"on":[86,116],"previous":[87],"theoretical":[88],"research,":[89],"paper":[91],"shows":[92],"regret":[93],"bound":[94],"FTRL-Proximal":[100],"algorithm":[101],"with":[102],"adaptive":[103],"learning":[104],"rate,":[105],"analyzes":[107],"effect":[109],"learning-rate":[111],"hyper-parameters":[112],"mini-batch":[114],"size":[115]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
