Learning with generalized negative dependence : probabilistic models of diversity for machine learning

This thesis establishes negative dependence as a powerful and computationally efficient framework to analyze machine learning problems that require a theoretical model of diversification. Examples of such problems include experimental design and model compression: subset-selection problems that require carefully balancing the quality of each selected element with the diversity of the subset as a whole. Negative dependence, which models the behavior of "repelling" random variables, provides a rich mathematical framework for the analysis of such problems. Leveraging negative dependence theory for machine learning requires (a) scalable sampling and learning algorithms for negatively dependent measures, and (b) negatively dependent measures able to model the specific diversity requirements that arise in machine learning. These problems are the focus of this thesis.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Mariet, Zelda Elaine.

Advisor dc:contributor.advisor

• Suvrit Sra.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122739

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122739