Conformal Methods for Efficient and Reliable Deep Learning
Name
fisch-fisch-phd-eecs-2023-thesis.pdf
Description
Thesis PDF
Size
10.01 MB
Format
Adobe PDF
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ae7c70d7d4eca75f103d8223c0a36e21
Author(s)
Fisch, Adam
Advisor(s)
Barzilay, Regina
Jaakkola, Tommi
Date Issued
September 2023
Publisher
Massachusetts Institute of Technology
Abstract
Deep learning has seen exciting progress over the last decade. As large foundation models continue to evolve and be deployed into real-life applications, an important question to ask is how we can make these expensive, inscrutable models more efficient and reliable. In this thesis, we present a number of fundamental techniques for building and deploying effective deep learning systems that are broadly based on conformal prediction, a model-agnostic and distribution-free uncertainty estimation framework. We develop both theory and practice for leveraging uncertainty estimation to build adaptive models that are cheaper to run, have desirable performance guarantees, and are general enough to work well in many real-world scenarios. Empirically, we primarily focus on natural language processing (NLP) applications, together with substantial extensions to tasks in computer vision, drug discovery, and medicine.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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