{"id":"https://openalex.org/W7123363216","doi":"https://doi.org/10.1109/cdc57313.2025.11312839","title":"Juggler: Multitask Learning with Task Performance Constraints","display_name":"Juggler: Multitask Learning with Task Performance Constraints","publication_year":2025,"publication_date":"2025-12-09","ids":{"openalex":"https://openalex.org/W7123363216","doi":"https://doi.org/10.1109/cdc57313.2025.11312839"},"language":null,"primary_location":{"id":"doi:10.1109/cdc57313.2025.11312839","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc57313.2025.11312839","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 64th Conference on Decision and Control (CDC)","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/A5122849759","display_name":"Ella Bar-El Avidan","orcid":null},"institutions":[{"id":"https://openalex.org/I16391192","display_name":"Tel Aviv University","ror":"https://ror.org/04mhzgx49","country_code":"IL","type":"education","lineage":["https://openalex.org/I16391192"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Ella Bar-El Avidan","raw_affiliation_strings":["Tel Aviv University,School of Electrical and Computer Engineering,Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tel Aviv University,School of Electrical and Computer Engineering,Israel","institution_ids":["https://openalex.org/I16391192"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020882454","display_name":"Ilai Bistritz","orcid":"https://orcid.org/0000-0002-4120-8292"},"institutions":[{"id":"https://openalex.org/I16391192","display_name":"Tel Aviv University","ror":"https://ror.org/04mhzgx49","country_code":"IL","type":"education","lineage":["https://openalex.org/I16391192"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Ilai Bistritz","raw_affiliation_strings":["Tel Aviv University,School of Industrial and Intelligent Systems Engineering and School of Electrical and Computer Engineering,Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tel Aviv University,School of Industrial and Intelligent Systems Engineering and School of Electrical and Computer Engineering,Israel","institution_ids":["https://openalex.org/I16391192"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.79296729,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8009","last_page":"8014"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.7437999844551086,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.7437999844551086,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.06610000133514404,"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"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.02419999986886978,"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/task","display_name":"Task (project management)","score":0.6707000136375427},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.6129000186920166},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.5967000126838684},{"id":"https://openalex.org/keywords/epoch","display_name":"Epoch (astronomy)","score":0.4480000138282776},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44029998779296875},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.4361000061035156},{"id":"https://openalex.org/keywords/balance","display_name":"Balance (ability)","score":0.41909998655319214}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.685699999332428},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6707000136375427},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.6129000186920166},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.5967000126838684},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5478000044822693},{"id":"https://openalex.org/C2780317896","wikidata":"https://www.wikidata.org/wiki/Q2703","display_name":"Epoch (astronomy)","level":3,"score":0.4480000138282776},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44029998779296875},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.4361000061035156},{"id":"https://openalex.org/C168031717","wikidata":"https://www.wikidata.org/wiki/Q1530280","display_name":"Balance (ability)","level":2,"score":0.41909998655319214},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.41679999232292175},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.40400001406669617},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34549999237060547},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3434000015258789},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32109999656677246},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3122999966144562},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.27559998631477356},{"id":"https://openalex.org/C2780312720","wikidata":"https://www.wikidata.org/wiki/Q5689100","display_name":"Head (geology)","level":2,"score":0.2565999925136566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cdc57313.2025.11312839","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc57313.2025.11312839","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 64th Conference on Decision and Control (CDC)","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":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Consider":[0],"multitask":[1],"learning":[2,142],"(MTL),":[3],"in":[4,82],"which":[5],"N":[6],"models":[7],"share":[8],"parts":[9],"of":[10,35,99,113,119],"their":[11],"architecture":[12],"(e.g.,":[13],"a":[14,17,40,89,130],"backbone":[15],"with":[16],"head":[18],"for":[19],"each":[20,36],"task).":[21],"Our":[22],"goal":[23],"is":[24,164],"to":[25,53,67,129,159],"train":[26],"the":[27,32,50,64,69,73,78,93,105,111,114,117,120,134,141],"overall":[28],"model":[29,131],"so":[30],"that":[31,75,91,124,132,161],"training":[33,95],"loss":[34,60,136],"task":[37],"falls":[38],"below":[39],"given":[41],"threshold.":[42,71],"However,":[43],"we":[44,87],"may":[45],"harm":[46],"others":[47],"when":[48],"varying":[49],"shared":[51],"parameters":[52],"help":[54],"one":[55],"task.":[56],"A":[57],"weighted":[58],"total":[59,94],"could":[61],"balance":[62,77],"between":[63],"different":[65],"tasks":[66,79,115],"achieve":[68],"target":[70,135],"Nevertheless,":[72],"weights":[74,106],"correctly":[76],"are":[80,150],"unknown":[81],"advance.":[83],"To":[84],"overcome":[85],"that,":[86],"propose":[88],"scheme":[90,103,126,163],"divides":[92],"time":[96],"into":[97],"epochs":[98],"increasing":[100],"length.":[101],"The":[102],"adjusts":[104],"every":[107],"epoch":[108,148],"based":[109],"on":[110,155],"performance":[112],"at":[116],"end":[118],"epoch.":[121],"We":[122,153],"prove":[123],"our":[125,162,168],"asymptotically":[127],"converges":[128],"satisfies":[133],"constraints":[137],"(if":[138],"feasible),":[139],"provided":[140],"rate,":[143],"control":[144],"step":[145],"size,":[146],"and":[147],"lengths":[149],"properly":[151],"tuned.":[152],"experiment":[154],"deep":[156],"neural":[157],"networks":[158],"demonstrate":[160],"effective":[165],"even":[166],"beyond":[167],"theoretical":[169],"assumptions.":[170]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-01-14T00:00:00"}