{"id":"https://openalex.org/W3200653887","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533992","title":"Model-Agnostic Explanations using Minimal Forcing Subsets","display_name":"Model-Agnostic Explanations using Minimal Forcing Subsets","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3200653887","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533992","mag":"3200653887"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9533992","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533992","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 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/A5102377539","display_name":"Xing Han","orcid":"https://orcid.org/0009-0004-9907-0988"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xing Han","raw_affiliation_strings":["University of Texas at Austin, Austin, USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, USA","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103071668","display_name":"Joydeep Ghosh","orcid":"https://orcid.org/0000-0002-7366-3548"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joydeep Ghosh","raw_affiliation_strings":["University of Texas at Austin, Austin, USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, USA","institution_ids":["https://openalex.org/I86519309"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5102377539"],"corresponding_institution_ids":["https://openalex.org/I86519309"],"apc_list":null,"apc_paid":null,"fwci":0.136,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.55182675,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"11","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9997000098228455,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9997000098228455,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9993000030517578,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9987000226974487,"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/computer-science","display_name":"Computer science","score":0.8077850341796875},{"id":"https://openalex.org/keywords/debugging","display_name":"Debugging","score":0.7040496468544006},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6450862884521484},{"id":"https://openalex.org/keywords/forcing","display_name":"Forcing (mathematics)","score":0.6361281871795654},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6288682818412781},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.5740787982940674},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.556465744972229},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5353721976280212},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.47891485691070557},{"id":"https://openalex.org/keywords/black-box","display_name":"Black box","score":0.4597734212875366},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09680646657943726}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8077850341796875},{"id":"https://openalex.org/C168065819","wikidata":"https://www.wikidata.org/wiki/Q845566","display_name":"Debugging","level":2,"score":0.7040496468544006},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6450862884521484},{"id":"https://openalex.org/C197115733","wikidata":"https://www.wikidata.org/wiki/Q1003136","display_name":"Forcing (mathematics)","level":2,"score":0.6361281871795654},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6288682818412781},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.5740787982940674},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.556465744972229},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5353721976280212},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.47891485691070557},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.4597734212875366},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09680646657943726},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9533992","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533992","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.699999988079071,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":74,"referenced_works":["https://openalex.org/W198744195","https://openalex.org/W1562353621","https://openalex.org/W2084701050","https://openalex.org/W2117539524","https://openalex.org/W2150165932","https://openalex.org/W2150480892","https://openalex.org/W2160536005","https://openalex.org/W2164878629","https://openalex.org/W2183341477","https://openalex.org/W2282821441","https://openalex.org/W2336525064","https://openalex.org/W2367397349","https://openalex.org/W2487898712","https://openalex.org/W2594475271","https://openalex.org/W2597603852","https://openalex.org/W2605409611","https://openalex.org/W2784560833","https://openalex.org/W2786004891","https://openalex.org/W2788403449","https://openalex.org/W2890457888","https://openalex.org/W2892285692","https://openalex.org/W2897133781","https://openalex.org/W2939984132","https://openalex.org/W2947285452","https://openalex.org/W2949574275","https://openalex.org/W2949605285","https://openalex.org/W2962769888","https://openalex.org/W2962851944","https://openalex.org/W2962862931","https://openalex.org/W2963067999","https://openalex.org/W2963125461","https://openalex.org/W2963343288","https://openalex.org/W2963420481","https://openalex.org/W2964134873","https://openalex.org/W2964303497","https://openalex.org/W2964330603","https://openalex.org/W2970631161","https://openalex.org/W2983687513","https://openalex.org/W2999615587","https://openalex.org/W3001400233","https://openalex.org/W3007233875","https://openalex.org/W3007590609","https://openalex.org/W3013149459","https://openalex.org/W3038644966","https://openalex.org/W3098054859","https://openalex.org/W3102272852","https://openalex.org/W3121410165","https://openalex.org/W3122175177","https://openalex.org/W3158021032","https://openalex.org/W4250589301","https://openalex.org/W4288414189","https://openalex.org/W4289704137","https://openalex.org/W4298140072","https://openalex.org/W6633888031","https://openalex.org/W6681608319","https://openalex.org/W6682385587","https://openalex.org/W6683971644","https://openalex.org/W6685133223","https://openalex.org/W6735632633","https://openalex.org/W6736518430","https://openalex.org/W6737947904","https://openalex.org/W6747609761","https://openalex.org/W6748426178","https://openalex.org/W6750462152","https://openalex.org/W6754041176","https://openalex.org/W6754204458","https://openalex.org/W6755399738","https://openalex.org/W6761184903","https://openalex.org/W6762234667","https://openalex.org/W6763182567","https://openalex.org/W6767935779","https://openalex.org/W6770151282","https://openalex.org/W6774451759","https://openalex.org/W6780778673"],"related_works":["https://openalex.org/W4321442002","https://openalex.org/W2015265939","https://openalex.org/W2284072287","https://openalex.org/W2155788121","https://openalex.org/W4235469518","https://openalex.org/W2387706296","https://openalex.org/W2480201319","https://openalex.org/W2611067230","https://openalex.org/W2377280071","https://openalex.org/W2809259768"],"abstract_inverted_index":{"How":[0],"can":[1,28],"we":[2,29,101,125],"find":[3],"a":[4,14,19,37,42,48,58,63,88,96,129,178],"subset":[5,79],"of":[6,51,77,90,122,131,169],"training":[7,52,82,137],"samples":[8,53,92],"that":[9,54,147,156],"are":[10,55],"most":[11],"responsible":[12],"for":[13,57,113],"specific":[15],"prediction":[16],"made":[17],"by":[18,94],"complex":[20],"black-box":[21],"machine":[22,170],"learning":[23,171],"model?":[24],"More":[25],"generally,":[26],"how":[27],"explain":[30],"the":[31,68,75,81,104,118,167],"model's":[32,60,69],"decisions":[33],"to":[34,46,128,158],"end-users":[35],"in":[36,172],"transparent":[38],"way?":[39],"We":[40],"propose":[41],"new":[43],"model-agnostic":[44],"algorithm":[45,85,105,149],"identify":[47],"minimal":[49],"set":[50,89,138],"indispensable":[56],"given":[59],"decision":[61,70],"at":[62],"particular":[64],"test":[65],"point,":[66],"i.e.,":[67],"would":[71],"have":[72],"changed":[73],"upon":[74],"removal":[76],"this":[78],"from":[80],"dataset.":[83],"Our":[84],"identifies":[86],"such":[87,175],"\u201cindispensable\u201d":[91],"iteratively":[93],"solving":[95],"constrained":[97],"optimization":[98],"problem.":[99],"Further,":[100],"speed":[102],"up":[103],"through":[106],"efficient":[107],"approximations":[108],"and":[109,120,140,153,164],"provide":[110],"theoretical":[111],"justification":[112],"its":[114],"performance.":[115],"To":[116],"demonstrate":[117],"applicability":[119],"effectiveness":[121],"our":[123,148],"approach,":[124],"apply":[126],"it":[127],"variety":[130],"tasks":[132],"including":[133],"data":[134],"poisoning":[135],"detection,":[136],"debugging":[139],"understanding":[141,176],"loan":[142],"decisions.":[143],"The":[144],"results":[145],"show":[146],"is":[150,177],"an":[151],"effective":[152],"easy-to-comprehend":[154],"tool":[155],"helps":[157],"better":[159],"understand":[160],"local":[161],"model":[162],"behavior,":[163],"therefore":[165],"facilitates":[166],"adoption":[168],"domains":[173],"where":[174],"requisite.":[179]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}