Understanding generalization
Name
1016455698-MIT.pdf
Description
Full printable version
Size
594.79 KB
Format
Adobe PDF
Checksum (MD5)
acf2445f686a0292f7ab25b1d0bcf3cd
Author(s)
Ong, Ming Yang
Advisor(s)
Pablo A. Parrilo.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
An important goal in machine learning is to understand how to design models that can generalize. This thesis follows a venerable line of research aimed at understanding generalization through the lens of stability- the study of how variations on the inputs of a system can cause its outputs to change. We explore stability and generalization in two different directions. In the first direction we look at proving stability using a proof technique provided by Hardt et al [HRS16]. We apply this technique to stochastic gradient descent with momentum and investigate the resulting stability bounds under some assumptions. In the second direction, we explore the effectiveness of stability in obtaining generalization bounds under the violation of some model assumptions. In particular, we show that stability is insufficient for generalization under domain adaptation. We introduce a sufficient condition and show that some properties can imply this condition.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 61-62).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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