Learning and optimization in the face of data perturbations
Name
1191230169-MIT.pdf
Size
4.3 MB
Format
Adobe PDF
Checksum (MD5)
c08ad0c79d00d0a7fb6876740a79a602
Author(s)
Staib, Matthew James.
Advisor(s)
Stefanie Jegelka.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Many problems in the machine learning pipeline boil down to maximizing the expectation of a function over a distribution. This is the classic problem of stochastic optimization. There are two key challenges in solving such stochastic optimization problems: 1) the function is often non-convex, making optimization difficult; 2) the distribution is not known exactly, but may be perturbed adversarially or is otherwise obscured. Each issue is individually so challenging to warrant a substantial accompanying body of work addressing it, but addressing them simultaneously remains difficult. This thesis addresses problems at the intersection of non-convexity and data perturbations. We study the intersection of the two issues along two dual lines of inquiry: first, we build perturbation-aware algorithms with guarantees for non-convex problems; second, we seek to understand how data perturbations can be leveraged to enhance non-convex optimization algorithms. Along the way, we will study new types of data perturbations and seek to understand their connection to generalization.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 145-163).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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