{"id":"https://openalex.org/W2953077209","doi":"https://doi.org/10.1145/3292500.3330904","title":"Robust Task Grouping with Representative Tasks for Clustered Multi-Task Learning","display_name":"Robust Task Grouping with Representative Tasks for Clustered Multi-Task Learning","publication_year":2019,"publication_date":"2019-07-25","ids":{"openalex":"https://openalex.org/W2953077209","doi":"https://doi.org/10.1145/3292500.3330904","mag":"2953077209"},"language":"en","primary_location":{"id":"doi:10.1145/3292500.3330904","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330904","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","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/A5026209764","display_name":"Yaqiang Yao","orcid":"https://orcid.org/0000-0002-5847-6672"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yaqiang Yao","raw_affiliation_strings":["University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060815634","display_name":"Jie Cao","orcid":"https://orcid.org/0000-0002-9942-3243"},"institutions":[{"id":"https://openalex.org/I137056471","display_name":"Nanjing University of Finance and Economics","ror":"https://ror.org/031y8am81","country_code":"CN","type":"education","lineage":["https://openalex.org/I137056471"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Cao","raw_affiliation_strings":["Nanjing University of Finance and Economics, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Finance and Economics, Nanjing, China","institution_ids":["https://openalex.org/I137056471"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100605447","display_name":"Huanhuan Chen","orcid":"https://orcid.org/0000-0002-3918-384X"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huanhuan Chen","raw_affiliation_strings":["University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5026209764"],"corresponding_institution_ids":["https://openalex.org/I126520041"],"apc_list":null,"apc_paid":null,"fwci":3.2159,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.92708068,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1408","last_page":"1417"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9966999888420105,"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":0.9861999750137329,"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/computer-science","display_name":"Computer science","score":0.7857601642608643},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.7364755272865295},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5796809196472168},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5747886896133423},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4861180782318115},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.48036178946495056},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.4467320740222931},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.43304958939552307},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4193265438079834},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.330088347196579},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1105966567993164}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7857601642608643},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.7364755272865295},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5796809196472168},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5747886896133423},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4861180782318115},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.48036178946495056},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.4467320740222931},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.43304958939552307},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4193265438079834},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.330088347196579},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1105966567993164},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3292500.3330904","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330904","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2658191610","display_name":null,"funder_award_id":"91846111,91746209","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":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1526543462","https://openalex.org/W1600006923","https://openalex.org/W1970554427","https://openalex.org/W2032612424","https://openalex.org/W2065180801","https://openalex.org/W2100556411","https://openalex.org/W2110332320","https://openalex.org/W2118099552","https://openalex.org/W2124541940","https://openalex.org/W2130903752","https://openalex.org/W2138019504","https://openalex.org/W2143104527","https://openalex.org/W2156392723","https://openalex.org/W2165698076","https://openalex.org/W2166180593","https://openalex.org/W2167474603","https://openalex.org/W2244253939","https://openalex.org/W2322020277","https://openalex.org/W2604738573","https://openalex.org/W2625093932","https://openalex.org/W2786432334","https://openalex.org/W2809623940","https://openalex.org/W2914584698","https://openalex.org/W2949664970","https://openalex.org/W3102963682","https://openalex.org/W4244393449","https://openalex.org/W4292363360"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4319309271","https://openalex.org/W4318718973","https://openalex.org/W4379958445","https://openalex.org/W4366320140","https://openalex.org/W2784094750","https://openalex.org/W4285160008","https://openalex.org/W1812322370","https://openalex.org/W2597787948","https://openalex.org/W3200098538"],"abstract_inverted_index":{"Multi-task":[0],"learning":[1,27,46,66,75],"aims":[2],"to":[3,31,48,148,152],"learn":[4],"multiple":[5],"tasks":[6,13,21,70,99,108,132],"jointly":[7],"by":[8,74,92,112],"sharing":[9,136],"information":[10,135],"among":[11,137],"related":[12,139],"such":[14],"that":[15,45,68,86,100],"the":[16,38,42,76,113,123,130,138,143,149,154,161,174,195,198],"generalization":[17],"performance":[18,33],"over":[19],"different":[20],"could":[22,128],"be":[23,90,110],"improved.":[24],"Although":[25],"multi-task":[26,65],"has":[28],"been":[29],"demonstrated":[30],"obtain":[32],"gain":[34],"in":[35],"comparison":[36],"with":[37,50],"single":[39],"task":[40,88],"learning,":[41],"main":[43,80],"challenge":[44],"what":[47],"share":[49],"whom":[51],"is":[52,85,146],"still":[53],"not":[54],"fully":[55],"resolved.":[56],"In":[57,141],"this":[58],"paper,":[59],"we":[60,166],"propose":[61],"a":[62,93,119],"robust":[63],"clustered":[64],"approach":[67,84,127],"clusters":[69],"into":[71],"several":[72],"groups":[73],"representative":[77,98,131],"tasks.":[78,104,140],"The":[79,105],"assumption":[81],"behind":[82],"our":[83,126,157],"each":[87],"can":[89,101,109],"represented":[91],"linear":[94],"combination":[95,115],"of":[96,156,178,197],"some":[97],"characterize":[102],"all":[103],"correlation":[106,124],"between":[107],"indicated":[111],"corresponding":[114],"coefficient.":[116],"By":[117],"imposing":[118],"row-sparse":[120],"constraint":[121],"on":[122,173,188],"matrix,":[125],"select":[129],"and":[133,180,190],"encourage":[134],"addition,":[142],"$l_1,2":[144],"$-norm":[145],"applied":[147],"representation":[150],"loss":[151],"enhance":[153],"robustness":[155],"approach.":[158,200],"To":[159],"solve":[160],"resulting":[162],"bi-convex":[163],"optimization":[164,170],"problem,":[165],"design":[167],"an":[168],"efficient":[169],"method":[171,177],"based":[172],"alternating":[175],"direction":[176],"multipliers":[179],"accelerated":[181],"proximal":[182],"gradient":[183],"method.":[184],"Finally,":[185],"experimental":[186],"results":[187],"synthetic":[189],"real-world":[191],"data":[192],"sets":[193],"validate":[194],"effectiveness":[196],"proposed":[199]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}