Kaiyu Yang will present his Pre FPO on Tuesday, February 22, 2022 at 10am in Friend Center 125 and Zoom (hybrid)

Zoom link: https://princeton.zoom.us/j/2731344683

Committee members: Jia Deng (examiner, adviser), Olga Russakovsky (examiner), Danqi Chen (examiner), Karthik Narasimhan (reader), Mayur Naik (reader, University of Pennsylvania)

Abstract: 

Symbolic reasoning is an important tool for modeling our world. For example, physics is essentially constructing symbolic models of the natural world, and engineering focuses on using such models to manipulate the world. Current machine learning is not capable of constructing such compact and powerful models, and is not capable of symbolic reasoning. Advances in symbolic reasoning can be revolutionary, potentially leading to machines that discover new math, sciences, and algorithms.

 

My long-term goal is to make machine learning capable of symbolic reasoning. And I try to combine the classical, symbolic approach to AI with the modern, machine learning–based approach. In this talk, I will present my work approaching this problem from two angles: (1) applying machine learning to symbolic reasoning tasks, such as automated theorem proving; (2) introducing symbolic components into machine learning models to make them more interpretable, verifiable, and data-efficient. The core idea behind my solutions is representing symbolic reasoning as proofs, which provides effective inductive biases for reasoning.