# EECS Special Seminar: Paul Krogmeier - Learning Symbolic Concepts and Domain-specific Languages

#### Speaker

Paul Krogmeier

University of Illinois Urbana-Champaign

#### Host

Professor Martin Rinard

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.

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.