{"id":"https://openalex.org/W7138261492","doi":"https://doi.org/10.1609/aaai.v40i23.39017","title":"A Robust Unlearning Method with Adaptive Knowledge Guidance and Memory Preservation","display_name":"A Robust Unlearning Method with Adaptive Knowledge Guidance and Memory Preservation","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138261492","doi":"https://doi.org/10.1609/aaai.v40i23.39017"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v40i23.39017","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i23.39017","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i23.39017","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129697659","display_name":"Jingyuan Tian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jingyuan Tian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129711264","display_name":"Xiaofei Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiaofei Zhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.41263058,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"23","first_page":"19398","last_page":"19405"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.6011000275611877,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.6011000275611877,"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":0.11349999904632568,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.053199999034404755,"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/process","display_name":"Process (computing)","score":0.41190001368522644},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.37439998984336853},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.3668000102043152},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.3077999949455261},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.28700000047683716}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7121999859809875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.508899986743927},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.41190001368522644},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3978999853134155},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.37439998984336853},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3668000102043152},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.3077999949455261},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.23759999871253967}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v40i23.39017","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i23.39017","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:ojs.aaai.org:article/39017","is_oa":false,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/39017","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i23.39017","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i23.39017","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7231209260","display_name":null,"funder_award_id":"62176252","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Machine":[0],"unlearning":[1,87,137,172],"has":[2],"emerged":[3],"as":[4,113],"a":[5,176],"promising":[6],"approach":[7],"to":[8,31,44,53,104,130,160,187],"remove":[9],"specific":[10,132],"knowledge":[11,65,83,114,120],"from":[12,179],"large":[13],"language":[14],"models":[15],"(LLMs),":[16],"especially":[17],"for":[18,27],"safety-critical":[19],"applications.":[20],"However,":[21],"existing":[22],"representation-based":[23,86,136],"methods":[24,41,88,202],"lack":[25,81],"guidance":[26,66,84],"selecting":[28],"representation":[29],"locations":[30],"unlearn":[32],"(RMU),":[33],"thus":[34],"lacking":[35],"precision":[36],"in":[37,85],"unlearning,":[38],"while":[39],"probability-based":[40],"are":[42],"vulnerable":[43],"fine-tuning":[45,94,168,207],"attacks":[46,95,169],"which":[47,78],"use":[48],"unrelated":[49],"and":[50,67,89,107,164],"safe":[51],"data":[52],"fine-tune":[54],"models.":[55,98],"To":[56],"address":[57],"these":[58,140],"problems,":[59],"this":[60,144],"paper":[61],"presents":[62],"an":[63],"adaptive":[64],"memory":[68,125],"perturbation":[69,145],"mechanisms,":[70],"called":[71],"ALMPU":[72,174,198],"(Adaptive":[73],"Localized":[74],"Memory":[75],"Perturbation":[76],"Unlearning)":[77],"addresses":[79],"the":[80,91,109,118,128,134,147,154,171,180,193],"of":[82,93,153,206],"mitigates":[90],"impact":[92],"on":[96,192],"unlearned":[97],"Specifically,":[99],"we":[100,122],"apply":[101],"scaling":[102],"factors":[103],"attention":[105,162],"heads":[106,163],"select":[108],"most":[110],"sensitive":[111,141],"ones":[112],"guidance.":[115],"Guided":[116],"by":[117],"previous":[119],"localization,":[121],"integrate":[123],"enhanced":[124],"perturbation\u2014which":[126],"forces":[127],"model":[129,148,182],"preserve":[131],"knowledge\u2014into":[133],"standard":[135],"process":[138],"at":[139],"positions.":[142],"Through":[143],"mechanism,":[146],"achieves":[149],"more":[150],"thorough":[151],"elimination":[152],"target":[155],"knowledge.":[156],"By":[157],"adding":[158],"interventions":[159],"selected":[161],"explicitly":[165],"optimizing":[166],"against":[167],"during":[170],"process,":[173],"creates":[175],"controlled":[177],"divergence":[178],"original":[181],"that":[183,197],"is":[184],"inherently":[185],"resistant":[186],"relearning":[188],"attempts.":[189],"Experimental":[190],"evaluation":[191],"WMDP":[194],"benchmark":[195],"demonstrates":[196],"consistently":[199],"outperforms":[200],"baseline":[201],"across":[203],"different":[204],"scales":[205],"attacks.":[208]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-18T00:00:00"}
