{"id":"https://openalex.org/W4415427954","doi":"https://doi.org/10.3233/faia251360","title":"Domain Generalization via Pareto Optimal Gradient Matching","display_name":"Domain Generalization via Pareto Optimal Gradient Matching","publication_year":2025,"publication_date":"2025-10-21","ids":{"openalex":"https://openalex.org/W4415427954","doi":"https://doi.org/10.3233/faia251360"},"language":null,"primary_location":{"id":"doi:10.3233/faia251360","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251360","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.3233/faia251360","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102646996","display_name":"Khoi Do","orcid":null},"institutions":[{"id":"https://openalex.org/I205274468","display_name":"Trinity College Dublin","ror":"https://ror.org/02tyrky19","country_code":"IE","type":"education","lineage":["https://openalex.org/I205274468"]}],"countries":["IE"],"is_corresponding":true,"raw_author_name":"Khoi Do","raw_affiliation_strings":["School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland","institution_ids":["https://openalex.org/I205274468"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063470952","display_name":"Ngoc-Thanh Le","orcid":null},"institutions":[{"id":"https://openalex.org/I94518387","display_name":"Hanoi University of Science and Technology","ror":"https://ror.org/04nyv3z04","country_code":"VN","type":"education","lineage":["https://openalex.org/I94518387"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Nam-Khanh Le","raw_affiliation_strings":["SoICT, Hanoi University of Science and Technology, Hanoi, Vietnam"],"affiliations":[{"raw_affiliation_string":"SoICT, Hanoi University of Science and Technology, Hanoi, Vietnam","institution_ids":["https://openalex.org/I94518387"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062525719","display_name":"Quoc\u2010Viet Pham","orcid":"https://orcid.org/0000-0002-9485-9216"},"institutions":[{"id":"https://openalex.org/I205274468","display_name":"Trinity College Dublin","ror":"https://ror.org/02tyrky19","country_code":"IE","type":"education","lineage":["https://openalex.org/I205274468"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Quoc-Viet Pham","raw_affiliation_strings":["School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland","institution_ids":["https://openalex.org/I205274468"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028533837","display_name":"Binh\u2010Son Hua","orcid":"https://orcid.org/0000-0002-5706-8634"},"institutions":[{"id":"https://openalex.org/I205274468","display_name":"Trinity College Dublin","ror":"https://ror.org/02tyrky19","country_code":"IE","type":"education","lineage":["https://openalex.org/I205274468"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Binh-Son Hua","raw_affiliation_strings":["School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland","institution_ids":["https://openalex.org/I205274468"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085192467","display_name":"Won\u2010Joo Hwang","orcid":"https://orcid.org/0000-0001-8398-564X"},"institutions":[{"id":"https://openalex.org/I4921948","display_name":"Pusan National University","ror":"https://ror.org/01an57a31","country_code":"KR","type":"education","lineage":["https://openalex.org/I4921948"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Won-Joo Hwang","raw_affiliation_strings":["Pusan National University, Busan, South Korea"],"affiliations":[{"raw_affiliation_string":"Pusan National University, Busan, South Korea","institution_ids":["https://openalex.org/I4921948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040660163","display_name":"Duong Nguyen","orcid":"https://orcid.org/0000-0003-1048-5825"},"institutions":[{"id":"https://openalex.org/I4210142044","display_name":"VinUniversity","ror":"https://ror.org/052dmdr17","country_code":"VN","type":"education","lineage":["https://openalex.org/I4210142044"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Duong Nguyen","raw_affiliation_strings":["College of Engineering & Computer Science, Vin University, Hanoi, Vietnam"],"affiliations":[{"raw_affiliation_string":"College of Engineering & Computer Science, Vin University, Hanoi, Vietnam","institution_ids":["https://openalex.org/I4210142044"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5102646996"],"corresponding_institution_ids":["https://openalex.org/I205274468"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.67975917,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9593999981880188,"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"}},"topics":[{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9593999981880188,"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/gradient-descent","display_name":"Gradient descent","score":0.732200026512146},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.608299970626831},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5728999972343445},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5565999746322632},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5392000079154968},{"id":"https://openalex.org/keywords/empirical-risk-minimization","display_name":"Empirical risk minimization","score":0.5153999924659729},{"id":"https://openalex.org/keywords/balanced-flow","display_name":"Balanced flow","score":0.4781000018119812},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.459199994802475},{"id":"https://openalex.org/keywords/gradient-method","display_name":"Gradient method","score":0.43959999084472656}],"concepts":[{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.732200026512146},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.608299970626831},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5728999972343445},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5565999746322632},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5392000079154968},{"id":"https://openalex.org/C107321475","wikidata":"https://www.wikidata.org/wiki/Q5374254","display_name":"Empirical risk minimization","level":2,"score":0.5153999924659729},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5005000233650208},{"id":"https://openalex.org/C167879884","wikidata":"https://www.wikidata.org/wiki/Q727568","display_name":"Balanced flow","level":2,"score":0.4781000018119812},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46239998936653137},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.459199994802475},{"id":"https://openalex.org/C115680565","wikidata":"https://www.wikidata.org/wiki/Q5977448","display_name":"Gradient method","level":2,"score":0.43959999084472656},{"id":"https://openalex.org/C81184566","wikidata":"https://www.wikidata.org/wiki/Q1191895","display_name":"Conjugate gradient method","level":2,"score":0.4172999858856201},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4133000075817108},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.40290001034736633},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38269999623298645},{"id":"https://openalex.org/C26362088","wikidata":"https://www.wikidata.org/wiki/Q17086453","display_name":"Nonlinear conjugate gradient method","level":4,"score":0.38260000944137573},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38089999556541443},{"id":"https://openalex.org/C10494615","wikidata":"https://www.wikidata.org/wiki/Q17086765","display_name":"Proximal Gradient Methods","level":4,"score":0.36899998784065247},{"id":"https://openalex.org/C2011187","wikidata":"https://www.wikidata.org/wiki/Q383851","display_name":"Directional derivative","level":2,"score":0.3409999907016754},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.33480000495910645},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.32269999384880066},{"id":"https://openalex.org/C111771559","wikidata":"https://www.wikidata.org/wiki/Q66295","display_name":"Derivative (finance)","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.304500013589859},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.2840000092983246},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.258899986743927}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/faia251360","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251360","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.3233/faia251360","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251360","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,83,108],"this":[1],"study,":[2],"we":[3,73,94,111],"address":[4],"the":[5,48,55,106,109,116,124,133],"gradient-based":[6],"domain":[7],"generalization":[8],"problem,":[9],"where":[10],"predictors":[11],"aim":[12],"for":[13],"consistent":[14],"gradient":[15,29,33,39,52,90,96,118,128,135,144],"directions":[16],"across":[17],"different":[18],"domains.":[19],"Existing":[20],"methods":[21,87],"have":[22],"two":[23],"main":[24],"challenges.":[25],"First,":[26],"minimization":[27,127],"of":[28,51],"empirical":[30,125],"distance":[31],"or":[32],"inner":[34],"products":[35],"(GIP)":[36],"leads":[37],"to":[38,54,65,85],"fluctuations":[40],"among":[41],"domains,":[42],"thereby":[43],"hindering":[44],"straightforward":[45],"learning.":[46],"Second,":[47],"direct":[49],"application":[50],"learning":[53],"joint":[56],"loss":[57],"function":[58],"can":[59,136],"incur":[60],"high":[61],"computation":[62],"overheads":[63],"due":[64],"second-order":[66],"derivative":[67],"approximation.":[68],"To":[69],"tackle":[70],"these":[71],"challenges,":[72],"propose":[74],"a":[75],"new":[76],"Pareto":[77],"Optimality":[78],"Gradient":[79],"Matching":[80],"(POGM)":[81],"method.":[82],"contrast":[84],"existing":[86],"that":[88],"add":[89],"matching":[91],"as":[92,98],"regularization,":[93],"leverage":[95],"trajectories":[97],"collected":[99],"data":[100],"and":[101],"apply":[102],"independent":[103],"training":[104],"at":[105,173],"meta-learner.":[107],"meta-update,":[110],"maximize":[112],"GIP":[113],"while":[114,165],"limiting":[115],"learned":[117],"from":[119,123,139,154],"deviating":[120],"too":[121],"far":[122],"risk":[126],"trajectory.":[129],"By":[130],"doing":[131],"so,":[132],"aggregate":[134],"incorporate":[137],"knowledge":[138],"all":[140],"domains":[141],"without":[142],"suffering":[143],"fluctuation":[145],"towards":[146],"any":[147],"particular":[148],"domain.":[149],"Experimental":[150],"evaluations":[151],"on":[152],"datasets":[153],"DomainBed":[155],"demonstrate":[156],"competitive":[157],"results":[158],"yielded":[159],"by":[160],"POGM":[161],"against":[162],"other":[163],"baselines":[164],"achieving":[166],"computational":[167],"efficiency.":[168],"The":[169],"code":[170],"is":[171],"available":[172],"https://github.com/skydvn/POGM.":[174]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-24T00:00:00"}
