Certifying robustness in inference and learning problems

There is a rich literature of algorithms for inference, prediction, and decision-making problems when the underlying distributions governing the data are known and well-modeled. The research from the past few decades has provided powerful learning algorithms when such distributions cannot be easily modeled, for instance with high dimensional data. As a result, such data driven methods are becoming ubiquitous in a wide variety of real-life applications, including safety-critical ones such as self-driving and medical diagnosis. In order for the reliable and safe deployment of such algorithms in practice, there exist several imminent questions to be answered. In this dissertation, a few topics in robustness of inference and learning methods are studied. One of the key issues with data-driven methods in practice is unexpected changes that can occur at inference time potentially affecting the performance of these methods, for instance, deviations in the data generating distributions, irrelevant or unrecognizable inputs, and adversarial attacks from unknown sources. The underlying theme connecting the topics studied in this dissertation is the development of learning algorithms robust to such unexpected, potentially harmful, deviations. Broadly, three problems in robust inference and learning are discussed in this dissertation - out-of-distribution detection for machine learning models, detection robust to distribution shifts, and multi-player multi-armed bandits robust to adversarial attacks. Principled approaches for these problems with theoretical guarantees are derived using tools from statistics, information theory and optimization, that are practical, resilient and efficiently implementable.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Magesh, Akshayaa

Contributors dc:contributor

• Veeravalli, Venugopal V.
• Rayadurgam, Srikant
• Raginsky, Maxim
• Shomorony, Ilan

• Out-of-Distribution Detection
• Robust Hypothesis Testing
• Distributional Robustness
• Multi-Player Multi-Armed Bandits
• Adversarial Robustness

Statement dc:rights

• Copyright 2025 Akshayaa Magesh

Language dc:language

eng, en

Handle dc:identifier

https://hdl.handle.net/2142/129164

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/129164