{"id":"https://openalex.org/W2787710446","doi":"https://doi.org/10.1109/ssci.2017.8280924","title":"Fine tuning lasso in an adversarial environment against gradient attacks","display_name":"Fine tuning lasso in an adversarial environment against gradient attacks","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2787710446","doi":"https://doi.org/10.1109/ssci.2017.8280924","mag":"2787710446"},"language":"en","primary_location":{"id":"doi:10.1109/ssci.2017.8280924","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2017.8280924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5079638101","display_name":"Gregory Ditzler","orcid":"https://orcid.org/0000-0001-6890-0935"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gregory Ditzler","raw_affiliation_strings":["Department of Electrical & Computer Engineering, University of Arizona, Tucson, AZ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, University of Arizona, Tucson, AZ, USA","institution_ids":["https://openalex.org/I138006243"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112261249","display_name":"Ashley Prater","orcid":null},"institutions":[{"id":"https://openalex.org/I1280414376","display_name":"United States Air Force Research Laboratory","ror":"https://ror.org/02e2egq70","country_code":"US","type":"facility","lineage":["https://openalex.org/I1280414376","https://openalex.org/I1330347796","https://openalex.org/I4210102105","https://openalex.org/I4389425425"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ashley Prater","raw_affiliation_strings":["Information Directorate, Air Force Research Laboratory, Rome, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Directorate, Air Force Research Laboratory, Rome, NY, USA","institution_ids":["https://openalex.org/I1280414376"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998000264167786,"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":0.9998000264167786,"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/T11324","display_name":"Spectroscopy Techniques in Biomedical and Chemical Research","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.993399977684021,"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/adversarial-system","display_name":"Adversarial system","score":0.7814127206802368},{"id":"https://openalex.org/keywords/lasso","display_name":"Lasso (programming language)","score":0.698478102684021},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6046000123023987},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42069652676582336},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.06329399347305298}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7814127206802368},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.698478102684021},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6046000123023987},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42069652676582336},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.06329399347305298}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssci.2017.8280924","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2017.8280924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1676820704","https://openalex.org/W1875482710","https://openalex.org/W1904826605","https://openalex.org/W1945616565","https://openalex.org/W1949281989","https://openalex.org/W1964538485","https://openalex.org/W1965052658","https://openalex.org/W1987875524","https://openalex.org/W1995196655","https://openalex.org/W2014466911","https://openalex.org/W2030611346","https://openalex.org/W2093331366","https://openalex.org/W2095195675","https://openalex.org/W2104094955","https://openalex.org/W2133864802","https://openalex.org/W2135046866","https://openalex.org/W2136051823","https://openalex.org/W2148143831","https://openalex.org/W2151298633","https://openalex.org/W2155344811","https://openalex.org/W2155648952","https://openalex.org/W2156504490","https://openalex.org/W2167421362","https://openalex.org/W2182464523","https://openalex.org/W2288481136","https://openalex.org/W2295755339","https://openalex.org/W2401293755","https://openalex.org/W2408703238","https://openalex.org/W2414043010","https://openalex.org/W2734657704","https://openalex.org/W2963207607","https://openalex.org/W2964275022","https://openalex.org/W3014813097","https://openalex.org/W4320931877","https://openalex.org/W6639467292","https://openalex.org/W6640425456","https://openalex.org/W6640718358","https://openalex.org/W6674005338","https://openalex.org/W6680007464","https://openalex.org/W6682686508","https://openalex.org/W6682926446","https://openalex.org/W6683024897","https://openalex.org/W6684559340","https://openalex.org/W6697196120","https://openalex.org/W6713706897","https://openalex.org/W6715815293"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W4246396837","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4310988119"],"abstract_inverted_index":{"Machine":[0],"learning":[1,78,125,212,249,256],"and":[2,11,59,108,265,291,309,316],"data":[3,13,131,153,180,196,224,264,286,308],"mining":[4],"algorithms":[5],"typically":[6],"assume":[7],"that":[8,160,236,281],"the":[9,17,36,46,52,55,75,80,103,118,142,146,151,164,168,176,185,188,208,240,254,260,283,288,295,303,311],"training":[10,128,147,285],"testing":[12,152],"are":[14],"sampled":[15],"from":[16,287],"same":[18],"fixed":[19,43],"probability":[20,44],"distribution;":[21],"however,":[22,51,184],"this":[23,39,243],"violation":[24],"is":[25,49,101,161,218],"often":[26],"violated":[27],"in":[28,73,79,123,155,163,207,253],"practice.":[29],"The":[30],"field":[31],"of":[32,41,77,82,91,106,121,167,178,187,195,210,239,262,294],"domain":[33,92,290],"adaptation":[34],"addresses":[35],"situation":[37],"where":[38,138],"assumption":[40],"a":[42,65,70,89,96,124,133,219,276],"between":[45,54,313],"two":[47,56],"domains":[48,57],"violated;":[50],"difference":[53],"(training/source":[58],"testing/target)":[60],"may":[61],"not":[62,162],"be":[63,230],"known":[64,292],"priori.":[66],"There":[67],"has":[68,141,172,203,226,237],"been":[69,113,205,227],"recent":[71,119],"thrust":[72],"addressing":[74],"problem":[76,90,280],"presence":[81],"an":[83,139,234,298],"adversary,":[84],"which":[85,217],"we":[86,245],"formulate":[87],"as":[88],"adaption":[93],"to":[94,136,144,157,174,193,222,229,267],"build":[95],"more":[97],"robust":[98],"classifier.":[99,169],"This":[100],"because":[102],"overall":[104],"security":[105],"classifiers":[107],"their":[109],"preprocessing":[110,194],"stages":[111],"have":[112],"called":[114],"into":[115],"question":[116],"with":[117],"findings":[120],"adversaries":[122],"setting.":[126],"Adversarial":[127],"(and":[129],"testing)":[130],"pose":[132],"serious":[134],"threat":[135],"scenarios":[137],"attacker":[140,235],"opportunity":[143],"\u201cpoison\u201d":[145],"or":[148,200],"\u201cevade\u201d":[149],"on":[150,181,190,306],"set(s)":[154],"order":[156],"achieve":[158],"something":[159],"best":[165],"interest":[166],"Recent":[170],"work":[171],"begun":[173],"show":[175,310],"impact":[177,186],"adversarial":[179,211,255,263,299],"several":[182],"classifiers;":[183],"adversary":[189],"aspects":[191],"related":[192],"(i.e.,":[197],"dimensionality":[198],"reduction":[199],"feature":[201],"selection)":[202],"widely":[204],"ignored":[206],"revamp":[209],"research.":[213],"Furthermore,":[214],"variable":[215],"selection,":[216],"vital":[220],"component":[221],"any":[223],"analysis,":[225],"shown":[228],"particularly":[231],"susceptible":[232],"under":[233],"knowledge":[238],"task.":[241],"In":[242],"work,":[244],"explore":[246],"avenues":[247],"for":[248,297],"resilient":[250],"classification":[251,314],"models":[252],"setting":[257],"by":[258],"considering":[259],"effects":[261,270],"how":[266],"mitigate":[268],"its":[269],"through":[271],"optimization.":[272],"Our":[273],"model":[274,296],"forms":[275],"single":[277],"convex":[278],"optimization":[279],"uses":[282],"labeled":[284],"source":[289],"weaknesses":[293],"component.":[300],"We":[301],"benchmark":[302],"proposed":[304],"approach":[305],"synthetic":[307],"trade-off":[312],"accuracy":[315],"skew-insensitive":[317],"statistics.":[318]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}