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dc.contributor.authorLee, Guang-He
dc.contributor.authorYuan, Yang
dc.contributor.authorJaakkola, Tommi S
dc.date.accessioned2021-01-19T15:26:13Z
dc.date.available2021-01-19T15:26:13Z
dc.date.issued2020-02
dc.date.submitted2019-06
dc.identifier.issn1049-5258
dc.identifier.urihttps://hdl.handle.net/1721.1/129439
dc.description.abstractStrong theoretical guarantees of robustness can be given for ensembles of classifiers generated by input randomization. Specifically, an `2 bounded adversary cannot alter the ensemble prediction generated by an additive isotropic Gaussian noise, where the radius for the adversary depends on both the variance of the distribution as well as the ensemble margin at the point of interest. We build on and considerably expand this work across broad classes of distributions. In particular, we offer adversarial robustness guarantees and associated algorithms for the discrete case where the adversary is `0 bounded. Moreover, we exemplify how the guarantees can be tightened with specific assumptions about the function class of the classifier such as a decision tree. We empirically illustrate these results with and without functional restrictions across image and molecule datasets.en_US
dc.language.isoen
dc.relation.isversionofhttps://papers.nips.cc/paper/2019/hash/fa2e8c4385712f9a1d24c363a2cbe5b8-Abstract.htmlen_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceNeural Information Processing Systems (NIPS)en_US
dc.titleTight certificates of adversarial robustness for randomly smoothed classifiersen_US
dc.typeArticleen_US
dc.identifier.citationLee, Guang-He et al. “Tight certificates of adversarial robustness for randomly smoothed classifiers.”32nd Conference on Neural Information Processing Systems, December 2018, Montreal, Canada, Neural Information Processing Systems, 2018. © 2018 The Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.relation.journal32nd Conference on Neural Information Processing Systems (NeurIPS 2018)en_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2020-12-21T16:05:25Z
dspace.orderedauthorsLee, GH; Yuan, Y; Chang, S; Jaakkola, TSen_US
dspace.date.submission2020-12-21T16:05:28Z
mit.journal.volume32en_US
mit.licensePUBLISHER_POLICY


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