Uncertainty quantification in machine learning with Bayesian models
Qian, Christopher
This item's files can only be accessed by the System Administrators group.
Permalink
https://hdl.handle.net/2142/125761
Description
Title
Uncertainty quantification in machine learning with Bayesian models
Author(s)
Qian, Christopher
Issue Date
2024-07-02
Director of Research (if dissertation) or Advisor (if thesis)
Liang, Feng
Doctoral Committee Chair(s)
Liang, Feng
Committee Member(s)
Li, Bo
Simpson, Douglas
Adams, Jason
Department of Study
Statistics
Discipline
Statistics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Recalibration
Epistemic Uncertainty
Dropout
Language
eng
Abstract
Uncertainty quantification plays a vital role to the adoption of modern machine learning methods in real-world applications by improving the trustworthiness and reliability of complex models. In the following chapters, we develop methods in uncertainty quantification that address several major areas of current research. The first two chapters focus on developing novel recalibration methods that can be applied to pre-trained models to improve their probabilistic predictions. In the classification setting, we extend the standard temperature scaling method by identifying one of its main characteristics of always increasing the uncertainty of the prediction and developing a method that enforces this property. In the regression setting, we introduce an optimization framework for optimization for which we can recover the well-known quantile recalibration method, and we use this framework to propose a novel method. In the third chapter, we propose a novel epistemic uncertainty quantification method and show that it faithfully targets the formal definition of epistemic uncertainty in terms of accuracy gain. All of our methods are designed with Bayesian methods in mind; the methods of Chapters 3 and 4 are specifically used with Bayesian models, and the method of Chapter 2 can be extended to Bayesian models.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
