Yuan Yang - Machine Learning PhD Student - School of Computational Science and Engineering
Date: December 2nd
Time: 11:00 AM – 1:00 PM EST
Location: Online
Meeting Link: https://gatech.zoom.us/j/94374751336
Committee
1 Dr. Faramarz Fekri, School of Electrical and Computer Engineering, Georgia Institute of Technology (Advisor)
2 Dr. Zsolt Kira, School of Interactive Computing, Georgia Institute of Technology
3 Dr. Larry Heck, School of Electrical and Computer Engineering, Georgia Institute of Technology
4 Dr. Viveck Cadambe, School of Electrical and Computer Engineering, Georgia Institute of Technology
5 Dr. Bo Dai, School of Computational Science and Engineering, Georgia Institute of Technology
Abstract
Modern machine learning models have provided new capabilities across a spectrum of applications in vision, reasoning, and natural language processing. However, these models are criticized for being non-interpretable, data-inefficient, and vulnerable to subtle perturbations such as adversarial attacks and distribution shifts. Addressing these issues remains at the center of developing trustworthy ML systems for real-world applications.
Our research focuses on providing a principled solution to these issues through logic reasoning formalism.
Specifically, we study the fundamental technique of inductive logic programming (ILP) that learns and represents patterns in knowledge graphs as first-order logic (FOL) rules, providing an interpretable approach to various reasoning tasks on structured data:
- we investigate the connection between model explanation and logic formalism and propose frameworks for explaining and defending ML models via logic reasoning;
- we formalize logic reasoning methods as a novel data programming paradigm and propose data-efficient frameworks for model training and evaluation;
- to improve the expressiveness of the ILP technique, we propose to extend the model to the temporal domain and hypergraphs so that one can generalize FOL rules on complex structures
Furthermore, our research explores the integration of large language models (LLMs) with logical reasoning techniques to enhance interpretability, data efficiency, and controllability in machine learning systems. We investigate:
- the potential of LLMs in translating natural language to formal logical representations to solve complex reasoning problems;
- enhancing LLMs' reasoning capability on open-ended, ambiguous problems by incorporating formal logic reasoning, thereby improving their controllability and robustness beyond narrowly defined domains.
By combining logic reasoning with the latest advancements in LLMs, our research aims to bridge the gap between powerful ML models and the need for explainable, efficient, and reliable AI systems in real-world applications.
