Northwestern University

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Learning under Adversarial Resilience

Description:

In this thesis we study two problems, one in unsupervised learning - k-means clustering and the other in a supervised learning setting with the presence of adversarial perturbations. We do a beyond-worst case style analysis and show that in either case instances that are resilient to adversarial perturbations are also...

Keyword:

Clustering, Margin Conditions, and Adversarial Learning

Subject:

Computer science

Creator:

Dutta, Abhratanu

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

02/01/2021

Date Created:

2020-01-01

Resource Type:

Dissertation

Alternate Identifier:

http://dissertations.umi.com/northwestern:15301 and etdadmin_upload_765296