{"id":"https://openalex.org/W2032612424","doi":"https://doi.org/10.1145/2339530.2339672","title":"Robust multi-task feature learning","display_name":"Robust multi-task feature learning","publication_year":2012,"publication_date":"2012-08-12","ids":{"openalex":"https://openalex.org/W2032612424","doi":"https://doi.org/10.1145/2339530.2339672","mag":"2032612424","pmid":"https://pubmed.ncbi.nlm.nih.gov/24078896","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/3783219"},"language":"en","primary_location":{"id":"doi:10.1145/2339530.2339672","doi":"https://doi.org/10.1145/2339530.2339672","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2339530.2339672","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/3783219","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050867621","display_name":"Pinghua Gong","orcid":"https://orcid.org/0009-0005-6889-6792","observed_orcids":["https://orcid.org/0009-0005-6889-6792"]},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pinghua Gong","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010419481","display_name":"Jieping Ye","orcid":"https://orcid.org/0000-0001-8662-5818","observed_orcids":["https://orcid.org/0000-0001-8662-5818"]},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jieping Ye","raw_affiliation_strings":["The Biodesign Institute, Arizona State University, Tempe, AZ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Biodesign Institute, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065063835","display_name":"Changshui Zhang","orcid":"https://orcid.org/0000-0002-8088-367X","observed_orcids":["https://orcid.org/0000-0002-8088-367X"]},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changshui Zhang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":57.0987,"has_fulltext":false,"cited_by_count":308,"citation_normalized_percentile":{"value":1.0,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":100},"biblio":{"volume":"2012","issue":null,"first_page":"895","last_page":"903"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9067000150680542,"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":0.9067000150680542,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.031700000166893005,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.013899999670684338,"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/shared-features","display_name":"shared features","score":0.6420000195503235},{"id":"https://openalex.org/keywords/group-lasso","display_name":"group lasso","score":0.8539999723434448},{"id":"https://openalex.org/keywords/accelerated-gradient-descent","display_name":"accelerated gradient descent","score":0.8119999766349792},{"id":"https://openalex.org/keywords/sparsity-pattern","display_name":"sparsity pattern","score":0.5270000100135803},{"id":"https://openalex.org/keywords/theoretical-limits","display_name":"theoretical bounds","score":0.48899999260902405},{"id":"https://openalex.org/keywords/robust-learning","display_name":"robust learning","score":0.40299999713897705}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7200502157211304},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6200124621391296},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5554865598678589},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5334596633911133},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5268943309783936},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4963257908821106},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.49287891387939453},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.48721736669540405},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.46053647994995117},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45482638478279114},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4325195550918579},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.41710615158081055},{"id":"https://openalex.org/C157553263","wikidata":"https://www.wikidata.org/wiki/Q5168004","display_name":"Coordinate descent","level":2,"score":0.4118993282318115},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.41110509634017944},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3740423917770386},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3538164496421814},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2972320318222046},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.138809472322464},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.1145/2339530.2339672","doi":"https://doi.org/10.1145/2339530.2339672","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2339530.2339672","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},{"id":"pmid:24078896","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/24078896","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","raw_type":"Journal Article"},{"id":"pmh:oai:europepmc.org:2772556","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/3783219","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:3783219","doi":null,"is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC3783219/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"KDD","raw_type":"Text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.364.9600","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.364.9600","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.public.asu.edu/~jye02/Publications/Papers/KDD12-pgong.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.364.9655","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.364.9655","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.public.asu.edu/~jye02/Publications/Papers/NIPS12_0978.pdf","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:europepmc.org:2772556","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/3783219","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G4278224465","display_name":"Computational Methods for Expression Image Analysis","funder_award_id":"R01LM010730","funder_id":"https://openalex.org/F4320337372","funder_display_name":"U.S. National Library of Medicine"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320337372","display_name":"U.S. National Library of Medicine","ror":"https://ror.org/0060t0j89"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W3219084","https://openalex.org/W26049824","https://openalex.org/W391578156","https://openalex.org/W1497745584","https://openalex.org/W1560550898","https://openalex.org/W1871180460","https://openalex.org/W1964233058","https://openalex.org/W2018096278","https://openalex.org/W2030290736","https://openalex.org/W2031250362","https://openalex.org/W2034295546","https://openalex.org/W2038082462","https://openalex.org/W2040649920","https://openalex.org/W2045704273","https://openalex.org/W2065180801","https://openalex.org/W2072988347","https://openalex.org/W2093545205","https://openalex.org/W2100556411","https://openalex.org/W2105180523","https://openalex.org/W2107838694","https://openalex.org/W2110994494","https://openalex.org/W2116581043","https://openalex.org/W2118099552","https://openalex.org/W2119187866","https://openalex.org/W2124541940","https://openalex.org/W2130903752","https://openalex.org/W2131479143","https://openalex.org/W2134982367","https://openalex.org/W2135624048","https://openalex.org/W2136920242","https://openalex.org/W2136979193","https://openalex.org/W2143104527","https://openalex.org/W2146482778","https://openalex.org/W2148522164","https://openalex.org/W2161034709","https://openalex.org/W2162888803","https://openalex.org/W2166721725","https://openalex.org/W2167474603","https://openalex.org/W2186054958","https://openalex.org/W2276230513","https://openalex.org/W2579721581","https://openalex.org/W2586353914","https://openalex.org/W2913340405","https://openalex.org/W2914746235","https://openalex.org/W2949664970","https://openalex.org/W2964036110","https://openalex.org/W3141595720","https://openalex.org/W4285719527","https://openalex.org/W6613554288"],"related_works":["https://openalex.org/W2018096278","https://openalex.org/W7099807918","https://openalex.org/W2186054958","https://openalex.org/W2149020531","https://openalex.org/W77289000","https://openalex.org/W7099038035","https://openalex.org/W2918165465","https://openalex.org/W2585181965","https://openalex.org/W2624439409","https://openalex.org/W2541573029"],"abstract_inverted_index":{"Multi-task":[0],"learning":[1,22],"(MTL)":[2],"aims":[3],"to":[4,34,153,159,175,197,211,245,248],"improve":[5],"the":[6,14,98,110,119,124,133,138,148,155,162,170,187,205,213,216,222,230,236,250],"performance":[7],"of":[8,50,86,118,147,219,275],"multiple":[9],"related":[10],"tasks":[11,45,65,90,104,257,281],"by":[12,227],"exploiting":[13],"intrinsic":[15],"relationships":[16],"among":[17,88,127,280],"them.":[18],"Recently,":[19],"multi-task":[20],"feature":[21],"algorithms":[23],"have":[24,30],"received":[25],"increasing":[26],"attention":[27],"and":[28,56,91,167,208,221,255,265,282],"they":[29,41],"been":[31],"successfully":[32],"applied":[33],"many":[35],"applications":[36],"involving":[37],"high-dimensional":[38],"data.":[39],"However,":[40],"assume":[42],"that":[43,169,229,269],"all":[44,103],"share":[46],"a":[47,73,83,182,194,241],"common":[48,84],"set":[49,85],"features,":[51],"which":[52,80],"is":[53,173,273],"too":[54],"restrictive":[55],"may":[57],"not":[58],"hold":[59],"in":[60,165],"real-world":[61,266],"applications,":[62],"since":[63],"outlier":[64,93,134,256,284],"often":[66],"exist.":[67],"In":[68,178],"this":[69],"paper,":[70],"we":[71,96,136,180,192,239],"propose":[72,152],"Robust":[74],"MultiTask":[75],"Feature":[76],"Learning":[77],"algorithm":[78,172],"(rMTFL)":[79],"simultaneously":[81,131,276],"captures":[82],"features":[87,126,254,279],"relevant":[89,128],"identifies":[92],"tasks.":[94,129,285],"Specifically,":[95,191],"decompose":[97],"weight":[99],"(model)":[100],"matrix":[101],"for":[102,122],"into":[105],"two":[106],"components.":[107],"We":[108,151],"impose":[109,137],"well-known":[111],"group":[112,140],"Lasso":[113,141],"penalty":[114,142],"on":[115,144,186,262],"row":[116],"groups":[117,146],"first":[120],"component":[121],"capturing":[123,277],"shared":[125,253,278],"To":[130],"identify":[132],"tasks,":[135],"same":[139],"but":[143],"column":[145],"second":[149],"component.":[150],"employ":[154],"accelerated":[156],"gradient":[157],"descent":[158],"efficiently":[160],"solve":[161],"optimization":[163],"problem":[164],"rMTFL,":[166],"show":[168,246],"proposed":[171,188,202,271],"scalable":[174],"large-size":[176],"problems.":[177],"addition,":[179],"provide":[181,209],"detailed":[183],"theoretical":[184,195,243],"analysis":[185],"rMTFL":[189,203,220,272],"formulation.":[190],"present":[193,240],"bound":[196],"measure":[198,212],"how":[199,247],"well":[200],"our":[201,270],"approximates":[204],"true":[206,224,232,252],"evaluation,":[207],"bounds":[210],"error":[214],"between":[215],"estimated":[217],"weights":[218,233],"underlying":[223,231,251],"weights.":[225],"Moreover,":[226],"assuming":[228],"are":[234],"above":[235],"noise":[237],"level,":[238],"sound":[242],"result":[244],"obtain":[249],"(sparsity":[258],"patterns).":[259],"Empirical":[260],"studies":[261],"both":[263],"synthetic":[264],"data":[267],"demonstrate":[268],"capable":[274],"identifying":[283]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":20},{"year":2020,"cited_by_count":45},{"year":2019,"cited_by_count":23},{"year":2018,"cited_by_count":28},{"year":2017,"cited_by_count":47},{"year":2016,"cited_by_count":24},{"year":2015,"cited_by_count":41},{"year":2014,"cited_by_count":26},{"year":2013,"cited_by_count":8},{"year":2012,"cited_by_count":1}],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2016-06-24T00:00:00"}