Deep Learning for High-Dimensional Decision Making and Uncertainty Quantification
Loading...
Author(s)
Repasky, Matthew
Advisor(s)
Editor(s)
Collections
Supplementary to:
Permanent Link
Abstract
The advent of deep learning has enabled the solving of problems of increasing complexity and dimension. Such problems include statistical hypothesis testing, learning policies for sequential-decision-making agents, and providing solutions to high-dimensional inverse problems. These problems can be associated with societal and scientific applications, including patrol and dispatch policies for emergency services and data recovery in planetary geophysics. In all such settings, deep neural networks can act as function approximators embedded in some part of a larger system. The immense expressive capacity of neural networks, in conjunction with advanced optimization techniques and computational resources, facilitate more efficient and effective solution of these complex problems.
The research outlined in this dissertation develops novel deep learning methodologies for solving complex problems related to decision-making and uncertainty quantification. First, a model diagnosis procedure is outlined, based on goodness-of-fit assessment using neural network critic functions. Second, a technique for learning joint policies of multiple decision-making agents with shared goals is described, with a particular application to police emergency services dispatch and patrol. Finally, a method for conditional sampling from generative models to solve inverse problems is covered. Inverse problems are highly relevant in the natural sciences; such a problem related to recovering planetary topography data is highlighted in this dissertation. The work outlined in this dissertation approaches these complex problems using deep learning tools, leveraging insight from computational statistics and operations research. This work demonstrates that embedding neural networks into larger frameworks for problem solving proves an effective tool for modern applications in science and society.
Sponsor
Date
2025-03-10
Extent
Resource Type
Text
Resource Subtype
Dissertation