Sponsor
Portland State University. Department of Mathematics and Statistics
First Advisor
Dacian Daescu
Term of Graduation
Spring 2026
Date of Publication
5-18-2026
Document Type
Dissertation
Degree Name
Doctor of Philosophy (Ph.D.) in Mathematical Sciences
Department
Mathematics and Statistics
Language
English
Subjects
Explainable AI, Mathematical Optimization, Neural Networks, Sensitivity Analysis
Physical Description
1 online resource (ix, 134 pages)
Abstract
Understanding how model predictions and training outcomes vary with changes in data, features, and modeling choices is central to explainable artificial intelligence. This dissertation introduces a unified framework for explainability by generalizing classical influence functions to encompass user-defined hyperparameters embedded in the training loss, model architecture, or data representation. By extending influence functions in this way, the framework broadens their applicability and integrates multiple explainability techniques into a single, coherent approach. It provides a common mathematical foundation linking data impact, feature importance, and model design analysis, and supports a broad class of additional explainability analyses beyond these settings. The demonstrated applications are validated across diverse neural network settings. In addition, we evaluate the practical feasibility of the proposed framework for large-scale models. Collectively, this work positions sensitivity analysis via hyperparameter-extended influence functions as a practical and scalable basis for explainability in modern machine learning systems.
Rights
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Persistent Identifier
https://archives.pdx.edu/ds/psu/44921
Recommended Citation
Breslin, William, "Improving Explainability and Interpretability of Neural Networks via Hyperparameter-Extended Influence Functions" (2026). Dissertations and Theses. Paper 7116.
Comments
This work was partially supported by the National Aeronautics and Space Administration (NASA) Research Opportunities in Space and Earth Sciences (ROSES2022) Program Element A.22 Weather and Atmospheric Dynamics under Grant Number 80NSSC23K1305.