EECS Special Seminar: 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.

Short 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.