{"id":"https://openalex.org/W4416251653","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227956","title":"Directed Perturbations for Efficient Learning of Surrogate Losses","display_name":"Directed Perturbations for Efficient Learning of Surrogate Losses","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251653","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227956"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227956","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227956","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5060392487","display_name":"Timothy R. Cargan","orcid":null},"institutions":[{"id":"https://openalex.org/I142263535","display_name":"University of Nottingham","ror":"https://ror.org/01ee9ar58","country_code":"GB","type":"education","lineage":["https://openalex.org/I142263535"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"T.R. Cargan","raw_affiliation_strings":["University of Nottingham,Computational Optimisation and Learning (COL) Lab, School of Computer Science,United Kingdom,NG8 1BB"],"affiliations":[{"raw_affiliation_string":"University of Nottingham,Computational Optimisation and Learning (COL) Lab, School of Computer Science,United Kingdom,NG8 1BB","institution_ids":["https://openalex.org/I142263535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037965570","display_name":"Dario Landa-Silva","orcid":"https://orcid.org/0000-0002-9499-6827"},"institutions":[{"id":"https://openalex.org/I142263535","display_name":"University of Nottingham","ror":"https://ror.org/01ee9ar58","country_code":"GB","type":"education","lineage":["https://openalex.org/I142263535"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"D. Landa-Silva","raw_affiliation_strings":["University of Nottingham,Computational Optimisation and Learning (COL) Lab, School of Computer Science,United Kingdom,NG8 1BB"],"affiliations":[{"raw_affiliation_string":"University of Nottingham,Computational Optimisation and Learning (COL) Lab, School of Computer Science,United Kingdom,NG8 1BB","institution_ids":["https://openalex.org/I142263535"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075541426","display_name":"Isaac Triguero","orcid":"https://orcid.org/0000-0002-0150-0651"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"I. Triguero","raw_affiliation_strings":["University of Granada,Department of Computer Science and Artificial Intelligence,Granada,Spain"],"affiliations":[{"raw_affiliation_string":"University of Granada,Department of Computer Science and Artificial Intelligence,Granada,Spain","institution_ids":["https://openalex.org/I173304897"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5060392487"],"corresponding_institution_ids":["https://openalex.org/I142263535"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36790264,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.15639999508857727,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.15639999508857727,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.10419999808073044,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.09210000187158585,"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/surrogate-model","display_name":"Surrogate model","score":0.6395000219345093},{"id":"https://openalex.org/keywords/regret","display_name":"Regret","score":0.5428000092506409},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.3982999920845032},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3547999858856201},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.34049999713897705},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.32839998602867126}],"concepts":[{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.6395000219345093},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5537999868392944},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.5428000092506409},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5234000086784363},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4194999933242798},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3982999920845032},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35519999265670776},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3547999858856201},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.34049999713897705},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32839998602867126},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29280000925064087},{"id":"https://openalex.org/C142806159","wikidata":"https://www.wikidata.org/wiki/Q7646876","display_name":"Surrogate data","level":3,"score":0.289900004863739},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.25529998540878296}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227956","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227956","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":12,"referenced_works":["https://openalex.org/W2891638642","https://openalex.org/W2982200588","https://openalex.org/W3112555615","https://openalex.org/W3136027957","https://openalex.org/W3174585579","https://openalex.org/W3189470799","https://openalex.org/W4251616545","https://openalex.org/W4293179730","https://openalex.org/W4293411891","https://openalex.org/W4392868640","https://openalex.org/W4393156974","https://openalex.org/W4402114587"],"related_works":[],"abstract_inverted_index":{"Decision-Focused":[0],"Learning":[1],"(DFL)":[2],"is":[3,53,102],"a":[4,14,36,43,64,72,124,130,137,146,156,170,181],"paradigm":[5],"to":[6,13,40,62,74,116,128],"learn":[7,47,75,169],"neural":[8],"network-based":[9],"predictive":[10],"models":[11],"tailored":[12],"specific":[15],"optimisation":[16,31,51],"problem.":[17],"A":[18,104],"key":[19],"challenge":[20],"for":[21,135],"DFL":[22],"methods":[23],"lies":[24],"in":[25],"the":[26,48,50,60,76,93,100,118,178],"non-differentiable":[27],"nature":[28],"of":[29,59,84,99,106,132,177],"most":[30],"problems.":[32],"Recent":[33],"solutions":[34],"use":[35],"learned,":[37],"differentiable,":[38],"model":[39],"act":[41],"as":[42,71],"surrogate":[44,77,119,138,172],"loss.":[45,78],"To":[46],"model,":[49],"problem":[52,149],"solved":[54],"repeatedly":[55],"using":[56,123],"random":[57,108],"perturbations":[58,109,134,168],"predictions":[61],"calculate":[63],"regret":[65],"value":[66],"which":[67],"can":[68],"be":[69],"used":[70],"target":[73],"However,":[79],"this":[80],"necessitates":[81],"numerous":[82],"runs":[83,176],"the,":[85],"potentially":[86],"computationally":[87],"expensive,":[88],"optimiser.":[89],"As":[90],"such,":[91],"maximising":[92],"useful":[94],"information":[95],"from":[96],"each":[97],"run":[98],"optimizer":[101],"paramount.":[103],"sample":[105],"purely":[107],"may":[110],"not":[111],"yield":[112],"an":[113],"effective":[114],"distribution":[115],"learning":[117,136],"from.":[120],"We":[121,141],"propose":[122],"directed":[125,167],"perturbation":[126],"strategy":[127],"generate":[129],"set":[131],"meaningful":[133],"loss":[139,173],"model.":[140],"evaluate":[142],"our":[143],"approach":[144],"on":[145,155],"resource":[147],"allocation":[148],"and":[150],"real-world":[151],"case":[152],"study":[153],"focused":[154],"critical":[157],"energy":[158],"challenge:":[159],"optimising":[160],"solar-plus-battery":[161],"systems.":[162],"The":[163],"results":[164],"show":[165],"that":[166],"stronger":[171],"with":[174],"fewer":[175],"optimiser,":[179],"enabling":[180],"more":[182],"efficient":[183],"DFL.":[184]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}