DC Field
Value
Language
dc.contributor.author
Li, Yannan
-
dc.contributor.author
Wang, Jingbo
-
dc.contributor.author
Wang, Chao
-
dc.contributor.editor
Griggio, Alberto
-
dc.contributor.editor
Rungta, Neha
-
dc.date.accessioned
2022-10-12T08:54:20Z
-
dc.date.available
2022-10-12T08:54:20Z
-
dc.date.issued
2022-10
-
dc.identifier.citation
<div class="csl-bib-body"> <div class="csl-entry">Li, Y., Wang, J., &#38; Wang, C. (2022). Proving Robustness of KNN Against Adversarial Data Poisoning. In A. Griggio &#38; N. Rungta (Eds.), <i>Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022</i> (pp. 7–16). TU Wien Academic Press. https://doi.org/10.34727/2022/isbn.978-3-85448-053-2_6</div> </div>
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/81318
-
dc.identifier.uri
https://doi.org/10.34727/2022/isbn.978-3-85448-053-2_6
-
dc.language.iso
en
-
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
-
dc.subject
formale Methode
de
dc.subject
formal methods
en
dc.title
Proving Robustness of KNN Against Adversarial Data Poisoning
en
dc.type
Konferenzbeitrag
de
dc.type
Inproceedings
en
dc.rights.license
Creative Commons Namensnennung 4.0 International
de
dc.rights.license
Creative Commons Attribution 4.0 International
en
dc.identifier.doi
10.34727/2022/isbn.978-3-85448-053-2_6
-
dc.contributor.affiliation
University of California, Los Angeles, United States of America (the)
-
dc.contributor.affiliation
Amazon (United States), United States of America (the)
-
dc.contributor.affiliation
University of California, Los Angeles, United States of America (the)
-
dc.contributor.editoraffiliation
Fondazione Bruno Kessler, Italy
-
dc.contributor.editoraffiliation
Amazon (United States), United States of America (the)
-
dc.relation.isbn
978-3-85448-053-2
-
dc.relation.doi
10.34727/2022/isbn.978-3-85448-053-2
-
dc.description.volume
3
-
dc.description.startpage
7
-
dc.description.endpage
16
-
dc.type.category
Full-Paper Contribution
-
dc.relation.eissn
2708-7824
-
tuw.booktitle
Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022
-
tuw.peerreviewed
true
-
tuw.relation.haspart
10.34727/2022/isbn.978-3-85448-053-2
-
tuw.relation.publisher
TU Wien Academic Press
-
tuw.relation.publisherplace
Wien
-
tuw.book.chapter
6
-
tuw.publication.orgunit
E192-04 - Forschungsbereich Formal Methods in Systems Engineering
-
dc.identifier.libraryid
AC17205284
-
dc.description.numberOfPages
10
-
tuw.relation.ispartoftuwseries
Conference Series: Formal Methods in Computer-Aided Design
-
dc.rights.identifier
CC BY 4.0
de
dc.rights.identifier
CC BY 4.0
en
item.languageiso639-1
en
-
item.openairetype
conference paper
-
item.grantfulltext
open
-
item.fulltext
with Fulltext
-
item.cerifentitytype
Publications
-
item.mimetype
application/pdf
-
item.openairecristype
http://purl.org/coar/resource_type/c_5794
-
item.openaccessfulltext
Open Access
-
crisitem.author.dept
University of California, Los Angeles
-
crisitem.author.dept
Amazon (United States)
-
crisitem.author.dept
University of California, Los Angeles
-
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