Paul Krogmeier – Learning Symbolic Concepts and Domain-specific Languages

Abstract
Symbolic languages are fundamental to computing: they help us understand and orchestrate unfamiliar concepts and computations in complex domains. Symbolic learning aims to synthesize concepts expressed in these languages, e.g., formulas or programs, given a few examples, with many applications in programming, testing, and verification of computer systems. Effective algorithms for symbolic learning rely on domain-specific heuristics, which makes them hard to build and limits application in new domains.

In this talk I will discuss my work on foundations of symbolic learning, which connects language semantics to uniform learning algorithms via an algorithmic meta-theorem. By writing specialized language interpreters, we are able to effectively describe learning algorithms and simultaneously prove new theorems about the decidability of learning in several well-studied symbolic languages in computer science. With this connection, I will explain how a fundamental technique based on version space algebra, as realized in program synthesizers from industry, e.g., Microsoft Excel’s FlashFill, is in fact an instance of a deeper concept related to tree automata. I will discuss how this connection between interpreters and algorithms uncovers a path to efficient specification and design of symbolic learning algorithms for new domains. I will also discuss my work on learning logical formulas and applications to visual discrimination and automated discovery of axiomatizations.

Finally, I will discuss my work on learning domain-specific languages (DSLs) for few-shot learning, which explores the problem of constructing DSLs that balance expressive power, succinctness, and tractability for effective symbolic learning in specific domains. I will conclude with some ideas for practically realizing an effective translation from interpreters to learning algorithms and some interesting applications of symbolic learning to music, math, and machine learning.

Bio
Paul Krogmeier is a PhD candidate at the University of Illinois Urbana-Champaign. Paul’s research is focused on algorithms for symbolic learning and the problem of learning symbolic languages and abstractions that capture specific domains. His work on symbolic learning was recognized with distinguished paper awards at POPL 2022 and OOPSLA 2023. He has also published in the areas of program synthesis, program verification, and differential privacy.

Details

• Date: Thursday, April 4
• Time: 11:00 am - 12:00 pm
• Category: Special Seminar
• Location: Grier Room A

Host

• Professor Martin Rinard