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Title: Optimization of Mutual Information in Learning: Explorations in Science
Authors: Strouse, Daniel
Advisors: Schwab, David J
Bialek, William
Contributors: Physics Department
Subjects: Artificial intelligence
Issue Date: 2018
Publisher: Princeton, NJ : Princeton University
Abstract: This thesis explores three applications of information theory in machine learning, all involving the optimization of information flow in some learning problem. In ChapterĀ 2, we introduce a method for extracting the most informative bits that one signal contains about another. Our method, the deterministic information bottleneck (DIB), is an alternative formulation of the information bottleneck (IB). In Chapter 3, we adapt the DIB to the problem of finding the most informative clusterings of geometric data. We also introduce an approach to model selection that naturally emerges within the (D)IB framework. In Chapter 4 we introduce an approach to encourage / discourage agents in a multi-agent reinforcement learning setting to share information with one another. We conclude in Chapter 5 by discussing ongoing and future work in these directions.
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog:
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Physics

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