{"id":"https://openalex.org/W4283204526","doi":"https://doi.org/10.1109/infocomwkshps54753.2022.9798378","title":"Collaborative Learning for Large-Scale Discrete Optimal Transport under Incomplete Populational Information","display_name":"Collaborative Learning for Large-Scale Discrete Optimal Transport under Incomplete Populational Information","publication_year":2022,"publication_date":"2022-05-02","ids":{"openalex":"https://openalex.org/W4283204526","doi":"https://doi.org/10.1109/infocomwkshps54753.2022.9798378"},"language":"en","primary_location":{"id":"doi:10.1109/infocomwkshps54753.2022.9798378","is_oa":false,"landing_page_url":"https://doi.org/10.1109/infocomwkshps54753.2022.9798378","pdf_url":null,"source":{"id":"https://openalex.org/S4363607985","display_name":"IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","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/A5016862635","display_name":"Navpreet Kaur","orcid":null},"institutions":[{"id":"https://openalex.org/I164389053","display_name":"Fordham University","ror":"https://ror.org/03qnxaf80","country_code":"US","type":"education","lineage":["https://openalex.org/I164389053"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Navpreet Kaur","raw_affiliation_strings":["Fordham University,Department of Computer and Information Sciences,New York,NY,USA,10023"],"affiliations":[{"raw_affiliation_string":"Fordham University,Department of Computer and Information Sciences,New York,NY,USA,10023","institution_ids":["https://openalex.org/I164389053"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100780558","display_name":"Juntao Chen","orcid":"https://orcid.org/0000-0001-7726-4926"},"institutions":[{"id":"https://openalex.org/I164389053","display_name":"Fordham University","ror":"https://ror.org/03qnxaf80","country_code":"US","type":"education","lineage":["https://openalex.org/I164389053"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Juntao Chen","raw_affiliation_strings":["Fordham University,Department of Computer and Information Sciences,New York,NY,USA,10023"],"affiliations":[{"raw_affiliation_string":"Fordham University,Department of Computer and Information Sciences,New York,NY,USA,10023","institution_ids":["https://openalex.org/I164389053"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5016862635"],"corresponding_institution_ids":["https://openalex.org/I164389053"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04029304,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9905999898910522,"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":0.9905999898910522,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9287999868392944,"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":0.9251000285148621,"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":0.6323455572128296},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.6266316771507263},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.37214571237564087},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3203452229499817},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12141650915145874},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.07640892267227173}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6323455572128296},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.6266316771507263},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.37214571237564087},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3203452229499817},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12141650915145874},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.07640892267227173}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/infocomwkshps54753.2022.9798378","is_oa":false,"landing_page_url":"https://doi.org/10.1109/infocomwkshps54753.2022.9798378","pdf_url":null,"source":{"id":"https://openalex.org/S4363607985","display_name":"IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W1594039573","https://openalex.org/W3159093377","https://openalex.org/W3175840110","https://openalex.org/W4206471589"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W4402327032","https://openalex.org/W2382290278"],"abstract_inverted_index":{"Optimal":[0],"transport":[1,81],"(OT)":[2],"is":[3,58,73,107],"a":[4,15,30,41,46,61,89,115,135],"framework":[5,44],"that":[6],"allows":[7],"for":[8],"optimal":[9],"allocation":[10],"of":[11,18,33,51,56,129],"limited":[12],"resources":[13],"in":[14,69,101],"network":[16],"consisting":[17],"sources":[19],"and":[20,48,75],"targets.":[21],"The":[22,54],"standard":[23],"OT":[24,43,122],"paradigm":[25],"does":[26],"not":[27,108],"extend":[28],"over":[29],"large":[31,47],"population":[32,50],"different":[34],"types.":[35],"In":[36],"this":[37],"paper,":[38],"we":[39,87,113],"establish":[40],"new":[42],"with":[45],"heterogeneous":[49],"target":[52],"nodes.":[53],"heterogeneity":[55],"targets":[57],"described":[59],"by":[60],"type":[62,105],"distribution":[63,72,106],"function.":[64],"We":[65,125],"consider":[66],"two":[67],"instances":[68],"which":[70,102],"the":[71,78,84,95,98,103,111,121,127,130],"known":[74],"unknown":[76],"to":[77,93,110,119],"sources,":[79,112],"i.e.,":[80],"designer.":[82],"For":[83,97],"former":[85],"case,":[86],"propose":[88],"fully":[90],"distributed":[91],"algorithm":[92,118,133],"obtain":[94],"solution.":[96],"latter":[99],"case":[100,136],"targets\u2019":[104],"available":[109],"develop":[114],"collaborative":[116],"learning":[117,132],"compute":[120],"scheme":[123],"efficiently.":[124],"evaluate":[126],"performance":[128],"proposed":[131],"using":[134],"study.":[137]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
