{"id":"https://openalex.org/W4391622449","doi":"https://doi.org/10.18653/v1/2026.findings-acl.2008","title":"Automatic Combination of Sample Selection Strategies for Few-Shot Learning","display_name":"Automatic Combination of Sample Selection Strategies for Few-Shot Learning","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W4391622449","doi":"https://doi.org/10.18653/v1/2026.findings-acl.2008"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2026.findings-acl.2008","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.2008","pdf_url":"https://aclanthology.org/2026.findings-acl.2008.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.findings-acl.2008.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078389036","display_name":"Branislav Pecher","orcid":"https://orcid.org/0000-0003-0344-8620"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Branislav Pecher","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082763244","display_name":"Ivan Srba","orcid":"https://orcid.org/0000-0003-3511-5337"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ivan Srba","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030414237","display_name":"M\u00e1ria Bielikov\u00e1","orcid":"https://orcid.org/0000-0003-4105-3494"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maria Bielikova","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5016794035","display_name":"Joaquin Vanschoren","orcid":"https://orcid.org/0000-0001-7044-9805"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Joaquin Vanschoren","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.00104723,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"40385","last_page":"40416"},"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":0.9611999988555908,"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":0.9611999988555908,"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/T12676","display_name":"Machine Learning and ELM","score":0.9466999769210815,"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/selection","display_name":"Selection (genetic algorithm)","score":0.7382885217666626},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.7101569771766663},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.6583392024040222},{"id":"https://openalex.org/keywords/one-shot","display_name":"One shot","score":0.5681411027908325},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5395995378494263},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4945778548717499},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41531333327293396},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1659468114376068},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.07193931937217712},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.05823424458503723},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.05506926774978638}],"concepts":[{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.7382885217666626},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.7101569771766663},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.6583392024040222},{"id":"https://openalex.org/C2992734406","wikidata":"https://www.wikidata.org/wiki/Q413267","display_name":"One shot","level":2,"score":0.5681411027908325},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5395995378494263},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4945778548717499},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41531333327293396},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1659468114376068},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.07193931937217712},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.05823424458503723},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.05506926774978638},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.18653/v1/2026.findings-acl.2008","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.2008","pdf_url":"https://aclanthology.org/2026.findings-acl.2008.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.tue.nl:openaire_cris_publications/4f95cd9c-b4d5-4b77-b380-41c034fd5fe7","is_oa":true,"landing_page_url":"https://research.tue.nl/en/publications/4f95cd9c-b4d5-4b77-b380-41c034fd5fe7","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Pecher, B, Srba, I, Bielikov\u00e1, M & Vanschoren, J 2024 'Automatic Combination of Sample Selection Strategies for Few-Shot Learning' arXiv.org. https://doi.org/10.48550/arXiv.2402.03038","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:arXiv.org:2402.03038","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2402.03038","pdf_url":"https://arxiv.org/pdf/2402.03038","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:zenodo.org:21213785","is_oa":true,"landing_page_url":"https://arxiv.org/abs/arXiv:2402.03038","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ACL 2026, Findings of the Association for Computational Linguistics, San Diego, California, United States, July 2\u20137, 2026","raw_type":"info:eu-repo/semantics/conferencePaper"},{"id":"doi:10.48550/arxiv.2402.03038","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2402.03038","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.findings-acl.2008","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.2008","pdf_url":"https://aclanthology.org/2026.findings-acl.2008.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G182602407","display_name":null,"funder_award_id":"952215","funder_id":"https://openalex.org/F4320334322","funder_display_name":"HORIZON EUROPE Framework Programme"},{"id":"https://openalex.org/G2151224667","display_name":"vera.ai: VERification Assisted by Artificial Intelligence","funder_award_id":"101070093","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G2592723113","display_name":null,"funder_award_id":"311070AKF2","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G434337789","display_name":null,"funder_award_id":"HORIZON2020","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G5378839026","display_name":null,"funder_award_id":"101070093","funder_id":"https://openalex.org/F4320334322","funder_display_name":"HORIZON EUROPE Framework Programme"},{"id":"https://openalex.org/G6816971739","display_name":null,"funder_award_id":"22-0414","funder_id":"https://openalex.org/F4320323251","funder_display_name":"Agent\u00fara na Podporu V\u00fdskumu a V\u00fdvoja"},{"id":"https://openalex.org/G6948331134","display_name":null,"funder_award_id":"APVV-22-","funder_id":"https://openalex.org/F4320323251","funder_display_name":"Agent\u00fara na Podporu V\u00fdskumu a V\u00fdvoja"},{"id":"https://openalex.org/G7381952728","display_name":"AI-CODE - AI services for COntinuous trust in emerging Digital Environments","funder_award_id":"101135437","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G7758855706","display_name":null,"funder_award_id":"311070AKF2","funder_id":"https://openalex.org/F4320335322","funder_display_name":"European Regional Development Fund"},{"id":"https://openalex.org/G7999996730","display_name":"Multimod\u00e1lna detekcia prejavov toxick\u00e9ho spr\u00e1vania v soci\u00e1lnych m\u00e9di\u00e1ch","funder_award_id":"APVV-22-0414","funder_id":"https://openalex.org/F4320323251","funder_display_name":"Agent\u00fara na Podporu V\u00fdskumu a V\u00fdvoja"},{"id":"https://openalex.org/G8079567275","display_name":"Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization","funder_award_id":"952215","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320323251","display_name":"Agent\u00fara na Podporu V\u00fdskumu a V\u00fdvoja","ror":"https://ror.org/037nx0e70"},{"id":"https://openalex.org/F4320334322","display_name":"HORIZON EUROPE Framework Programme","ror":null},{"id":"https://openalex.org/F4320335322","display_name":"European Regional Development Fund","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4391622449.pdf","grobid_xml":"https://content.openalex.org/works/W4391622449.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2497720472","https://openalex.org/W4292659306","https://openalex.org/W3044321615","https://openalex.org/W4294892107","https://openalex.org/W2806221744","https://openalex.org/W2326937258","https://openalex.org/W394267150","https://openalex.org/W2357748469","https://openalex.org/W2392917037","https://openalex.org/W2046675961"],"abstract_inverted_index":{"In":[0],"few-shot":[1,89,93],"learning,":[2],"the":[3,12,15,60,72,80,106,111,126,143,158],"selection":[4,19,67,77,118,135],"of":[5,14,53,64,74,82,108,160],"samples":[6],"has":[7],"a":[8,47,148],"significant":[9],"impact":[10,73],"on":[11,27,79,122,139],"performance":[13,81],"model.While":[16],"effective":[17,137],"sample":[18,76,134],"strategies":[20,35,78,109,119],"are":[21,151],"well-established":[22,66],"in":[23],"supervised":[24],"settings,":[25],"research":[26],"large":[28],"language":[29],"models":[30,86],"largely":[31],"overlooks":[32],"them,":[33],"favouring":[34],"specifically":[36],"tailored":[37],"to":[38,58],"individual":[39,117],"in-context":[40,84,127],"learning":[41,85,90,128],"settings.In":[42],"this":[43],"paper,":[44],"we":[45,131],"propose":[46],"new":[48],"method":[49,113],"for":[50],"Automatic":[51],"Combination":[52],"SamplE":[54],"Selection":[55],"Strategies":[56],"(ACSESS)":[57],"leverage":[59],"strengths":[61],"and":[62,70,87,98,120],"complementarity":[63],"various":[65],"objectives.We":[68],"investigate":[69],"compare":[71],"23":[75],"5":[83],"3":[88],"approaches":[91],"(meta-learning,":[92],"fine-tuning)":[94],"over":[95],"6":[96],"text":[97],"8":[99],"image":[100],"datasets.The":[101],"experimental":[102],"results":[103],"show":[104],"that":[105,133],"combination":[107],"through":[110],"ACSESS":[112],"consistently":[114],"outperforms":[115],"all":[116],"performs":[121],"par":[123],"or":[124],"exceeds":[125],"specific":[129],"baselines.Lastly,":[130],"demonstrate":[132],"remains":[136],"even":[138],"smaller":[140],"datasets,":[141],"yielding":[142],"greatest":[144],"benefits":[145],"when":[146],"only":[147],"few":[149],"shots":[150,161],"selected,":[152],"while":[153],"its":[154],"advantage":[155],"diminishes":[156],"as":[157],"number":[159],"increases.":[162]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2024-02-08T00:00:00"}
