{"id":"https://openalex.org/W4372260146","doi":"https://doi.org/10.1109/icassp49357.2023.10097096","title":"A Nested Ensemble Method to Bilevel Machine Learning","display_name":"A Nested Ensemble Method to Bilevel Machine Learning","publication_year":2023,"publication_date":"2023-05-05","ids":{"openalex":"https://openalex.org/W4372260146","doi":"https://doi.org/10.1109/icassp49357.2023.10097096"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49357.2023.10097096","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icassp49357.2023.10097096","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5091442724","display_name":"Lisha Chen","orcid":"https://orcid.org/0000-0001-8980-5275"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lisha Chen","raw_affiliation_strings":["Rensselaer Polytechnic Institute,Electrical, Computer, and Systems Engineering Department,Troy,NY,United States","Electrical, Computer, and Systems Engineering Department, Rensselaer Polytechnic Institute, Troy, NY, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute,Electrical, Computer, and Systems Engineering Department,Troy,NY,United States","institution_ids":["https://openalex.org/I165799507"]},{"raw_affiliation_string":"Electrical, Computer, and Systems Engineering Department, Rensselaer Polytechnic Institute, Troy, NY, United States","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033844485","display_name":"Momin Abbas","orcid":null},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Momin Abbas","raw_affiliation_strings":["Rensselaer Polytechnic Institute,Electrical, Computer, and Systems Engineering Department,Troy,NY,United States","Electrical, Computer, and Systems Engineering Department, Rensselaer Polytechnic Institute, Troy, NY, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute,Electrical, Computer, and Systems Engineering Department,Troy,NY,United States","institution_ids":["https://openalex.org/I165799507"]},{"raw_affiliation_string":"Electrical, Computer, and Systems Engineering Department, Rensselaer Polytechnic Institute, Troy, NY, United States","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100783476","display_name":"Tianyi Chen","orcid":"https://orcid.org/0000-0003-3477-1439"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianyi Chen","raw_affiliation_strings":["Rensselaer Polytechnic Institute,Electrical, Computer, and Systems Engineering Department,Troy,NY,United States","Electrical, Computer, and Systems Engineering Department, Rensselaer Polytechnic Institute, Troy, NY, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute,Electrical, Computer, and Systems Engineering Department,Troy,NY,United States","institution_ids":["https://openalex.org/I165799507"]},{"raw_affiliation_string":"Electrical, Computer, and Systems Engineering Department, Rensselaer Polytechnic Institute, Troy, NY, United States","institution_ids":["https://openalex.org/I165799507"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I165799507"],"apc_list":null,"apc_paid":null,"fwci":0.1613,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.52533826,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"5"},"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":0.9994000196456909,"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":0.9994000196456909,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9894999861717224,"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/T12209","display_name":"Bone and Joint Diseases","score":0.9785000085830688,"subfield":{"id":"https://openalex.org/subfields/2732","display_name":"Orthopedics and Sports Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.8063596487045288},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.732294499874115},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.705869197845459},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.6871547698974609},{"id":"https://openalex.org/keywords/bilevel-optimization","display_name":"Bilevel optimization","score":0.6767609119415283},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.620089054107666},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6015729904174805},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.46453624963760376},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.4479517340660095},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3585309088230133},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.26140862703323364},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20177128911018372},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.19702434539794922},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.18607398867607117}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.8063596487045288},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.732294499874115},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.705869197845459},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6871547698974609},{"id":"https://openalex.org/C3309286","wikidata":"https://www.wikidata.org/wiki/Q4907693","display_name":"Bilevel optimization","level":3,"score":0.6767609119415283},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.620089054107666},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6015729904174805},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.46453624963760376},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.4479517340660095},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3585309088230133},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26140862703323364},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20177128911018372},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.19702434539794922},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.18607398867607117},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49357.2023.10097096","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icassp49357.2023.10097096","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"No poverty","id":"https://metadata.un.org/sdg/1","score":0.5899999737739563}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1534477342","https://openalex.org/W1811750039","https://openalex.org/W2270245739","https://openalex.org/W2566079294","https://openalex.org/W2604763608","https://openalex.org/W2778234499","https://openalex.org/W2787415863","https://openalex.org/W2792422168","https://openalex.org/W2805481182","https://openalex.org/W2951104886","https://openalex.org/W2963122491","https://openalex.org/W2963238274","https://openalex.org/W2963341924","https://openalex.org/W2992525328","https://openalex.org/W3020429927","https://openalex.org/W3035016095","https://openalex.org/W3092809425","https://openalex.org/W3098341014","https://openalex.org/W3104267265","https://openalex.org/W3169366835","https://openalex.org/W3174470190","https://openalex.org/W3209390122","https://openalex.org/W3213144721","https://openalex.org/W3213782029","https://openalex.org/W4286893055","https://openalex.org/W4287328765","https://openalex.org/W4287331012","https://openalex.org/W4295292688","https://openalex.org/W6638214083","https://openalex.org/W6693919493","https://openalex.org/W6717697761","https://openalex.org/W6730042731","https://openalex.org/W6734265508","https://openalex.org/W6736057607","https://openalex.org/W6747447992","https://openalex.org/W6748676082","https://openalex.org/W6749700008","https://openalex.org/W6752515464","https://openalex.org/W6766092863","https://openalex.org/W6771260093","https://openalex.org/W6771378952","https://openalex.org/W6779138978","https://openalex.org/W6780651745","https://openalex.org/W6784259104","https://openalex.org/W6784314759","https://openalex.org/W6785955517","https://openalex.org/W6790226045","https://openalex.org/W6796490870","https://openalex.org/W6803117125","https://openalex.org/W6803136028","https://openalex.org/W6803236494","https://openalex.org/W6803515894","https://openalex.org/W7034108470"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W4390421286","https://openalex.org/W2482350142","https://openalex.org/W4280563792","https://openalex.org/W2140186469","https://openalex.org/W4246396837","https://openalex.org/W3126451824","https://openalex.org/W4297800546","https://openalex.org/W2804529069","https://openalex.org/W2963306862"],"abstract_inverted_index":{"Modern":[0],"machine":[1],"learning":[2,16],"problems,":[3,28],"such":[4],"as":[5],"hyperparameter":[6],"optimization,":[7],"meta":[8,72],"learning,":[9],"and":[10,26,60,77,82],"adversarial":[11],"training,":[12],"adopt":[13],"a":[14,21,55],"bilevel":[15,50],"formulation.":[17],"Such":[18],"problems":[19],"involve":[20],"nested":[22,56],"relation":[23],"between":[24],"inner-":[25],"outer-level":[27],"which":[29],"often":[30],"have":[31],"suboptimal":[32],"solutions":[33],"with":[34,71],"poor":[35],"generalization":[36,95],"ability.":[37],"To":[38],"address":[39],"this":[40,42],"issue,":[41],"paper":[43],"proposes":[44],"an":[45],"ensemble":[46,57,90],"method":[47,53],"tailored":[48],"to":[49,93],"learning.":[51,73],"Our":[52],"finds":[54],"of":[58,87],"inner":[59],"outer":[61],"parameters":[62],"that":[63,79],"improve":[64],"generalization.":[65],"We":[66,74],"instantiate":[67],"our":[68],"general":[69],"results":[70],"show":[75],"theoretically":[76],"empirically":[78],"the":[80,83,88,97],"diversity":[81],"smoother":[84],"loss":[85],"landscape":[86],"proposed":[89],"methods":[91],"lead":[92],"improved":[94],"over":[96],"state-of-the-art":[98],"method.":[99]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
