{"id":"https://openalex.org/W7128491495","doi":"https://doi.org/10.48550/arxiv.2602.07218","title":"Collaborative and Efficient Fine-tuning: Leveraging Task Similarity","display_name":"Collaborative and Efficient Fine-tuning: Leveraging Task Similarity","publication_year":2026,"publication_date":"2026-02-06","ids":{"openalex":"https://openalex.org/W7128491495","doi":"https://doi.org/10.48550/arxiv.2602.07218"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.07218","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125557795","display_name":"Gagik Magakyan","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Magakyan, Gagik","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059780399","display_name":"Amirhossein Reisizadeh","orcid":"https://orcid.org/0000-0002-1730-8402"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Reisizadeh, Amirhossein","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125555357","display_name":"Chanwoo Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Chanwoo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Parrilo, Pablo A.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Parrilo, Pablo A.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125562041","display_name":"Asuman Ozdaglar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ozdaglar, Asuman","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5125557795"],"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":null,"last_page":null},"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":0.3107999861240387,"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":0.3107999861240387,"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/T10028","display_name":"Topic Modeling","score":0.23469999432563782,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.10869999974966049,"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/leverage","display_name":"Leverage (statistics)","score":0.6050000190734863},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5705999732017517},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.5522000193595886},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4936999976634979},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.43149998784065247},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.3898000121116638},{"id":"https://openalex.org/keywords/adapter","display_name":"Adapter (computing)","score":0.3727000057697296},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.35040000081062317},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.325300008058548}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7807999849319458},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6050000190734863},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5705999732017517},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.5522000193595886},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4936999976634979},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4803999960422516},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4781000018119812},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.43149998784065247},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3898000121116638},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.3727000057697296},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.35040000081062317},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.325300008058548},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3246999979019165},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.32420000433921814},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31700000166893005},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.30880001187324524},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3077000081539154},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.3050999939441681},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C61423126","wikidata":"https://www.wikidata.org/wiki/Q187432","display_name":"Scripting language","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.27230000495910645},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2542000114917755},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.07218","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.07218","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.07218","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.07218","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"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":{"Adaptability":[0],"has":[1],"been":[2],"regarded":[3],"as":[4,25],"a":[5],"central":[6],"feature":[7],"in":[8,47,74,103],"the":[9,76],"foundation":[10,33,50,98],"models,":[11,51],"enabling":[12],"them":[13],"to":[14,17,54,70,92,106,122],"effectively":[15],"acclimate":[16],"unseen":[18],"downstream":[19,60],"tasks.":[20,124],"Parameter-efficient":[21],"fine-tuning":[22,48,78],"methods":[23],"such":[24],"celebrated":[26],"LoRA":[27],"facilitate":[28],"efficient":[29],"adaptation":[30],"of":[31,49],"large":[32],"models":[34],"using":[35],"labeled,":[36],"high-quality":[37],"and":[38,94,118,133],"generally":[39],"scarce":[40],"task":[41,56,90,113,150],"data.":[42],"To":[43],"mitigate":[44],"data":[45,79],"scarcity":[46],"we":[52],"propose":[53,82],"leverage":[55],"similarity":[57,91],"across":[58,115],"multiple":[59],"users.":[61],"Intuitively,":[62],"users":[63],"with":[64,148,159],"similar":[65,160],"tasks":[66],"must":[67],"be":[68],"able":[69],"assist":[71],"each":[72],"other":[73],"boosting":[75],"effective":[77],"size.":[80],"We":[81,125,141],"Collaborative":[83],"Low-Rank":[84],"Adaptation,":[85],"or":[86],"CoLoRA,":[87],"which":[88,152],"exploits":[89],"collaboratively":[93],"efficiently":[95],"fine-tune":[96],"personalized":[97,119],"models.":[99],"The":[100],"main":[101],"idea":[102],"CoLoRA":[104,128],"is":[105],"train":[107],"one":[108],"shared":[109],"adapter":[110],"capturing":[111],"underlying":[112],"similarities":[114],"all":[116],"tasks,":[117,161],"adapters":[120],"tailored":[121],"user-specific":[123],"theoretically":[126],"study":[127],"on":[129],"heterogeneous":[130],"linear":[131],"regression":[132],"provide":[134],"provable":[135],"guarantees":[136],"for":[137],"ground":[138],"truth":[139],"recovery.":[140],"also":[142],"conduct":[143],"several":[144],"natural":[145],"language":[146],"experiments":[147],"varying":[149],"similarity,":[151],"further":[153],"demonstrate":[154],"that":[155],"when":[156],"trained":[157],"together":[158],"individual":[162],"performances":[163],"are":[164],"significantly":[165],"boosted.":[166]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-11T00:00:00"}