Abstract

While supervised learning has seen great success in the modern machine learning era, the challenge of obtaining high-quality labeled training data still exists. In many knowledge-rich domains, we still face the problem of letting machine learning models learn well using a limited number of labels. Active learning (AL) has been a prominent learning paradigm that deals with such problems. This thesis reviews the classical challenges of AL, summarizes how our prior work has advanced the field, and charts a course for adapting AL to realistic and challenging scenarios. We begin by discussing past work on standard scenarios for AL, including multi-class and multi-label problems. In these traditional settings, we focus on uncertainty quantification, small data modeling, and balancing exploration and exploitation. We then explore emerging topics that address challenges related to data scalability, label noise, and limited evaluation budgets. Building on this foundation, we propose thesis projects aimed at enhancing current research and expanding its scope. Within the expanded scope of real-world AL challenges, we have proposed and studied the following important projects towards building a foundational framework: evidential AL for multi-label models, adaptive AL principles for noisy label learning, and principled active testing-while-learning frameworks. Later, we further apply AL methods to physics-informed machine learning models. Through these efforts, we aim to advance the methodology and application of AL, laying the groundwork for data-efficient, trustworthy, and scalable machine-learning systems.

Library of Congress Subject Headings

Machine learning; Data mining; Active learning

Publication Date

8-2025

Document Type

Dissertation

Student Type

Graduate

Degree Name

Computing and Information Sciences (Ph.D.)

Department, Program, or Center

Computing and Information Sciences Ph.D, Department of

College

Golisano College of Computing and Information Sciences

Advisor

Qi Yu

Advisor/Committee Member

Haibo Yang

Advisor/Committee Member

Rui Li

Campus

RIT – Main Campus

Plan Codes

COMPIS-PHD

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