Recently, Machine Learning (ML) methods have achieved spectacular results in solving difficult and highly relevant problems
like natural language (NL) translation, NL dialogue generation, object identification in photos, etc.
In this session, the interaction of ML methods with Symbolic Computation (SC) methods will be studied:
 the application of ML methods to improve SC methods,
 the application of SC methods to improve ML methods,
 the collaboration of SC methods and ML to create new powerful algorithms for hard problems.
Examples of topics falling into the scope of the session are given below. Talks focusing exclusively
on either ML or SC will not be considered.
Organizers
Examples of Topics
 semiautomating mathematical theory exploration (concept formation, conjecture formation, proving, etc.)
by combining SC and ML (LLMs) methods,
 semiautomating software development from NL specifications by combining SC and ML (LLMs) methods,
 combining ML and SC methods in systematic testing,
 translation between formal mathematical text and NL mathematical text
using ML (e.g. relation extraction from NL text),
 mathematical knowledge management (e.g. formulae retrieval in math knowledge bases) using ML,
 generating suggestions for next steps (predicting useful lemmas, proof strategies, resolvents, etc.)
in theorem proving (e.g. resolution proving) by ML,
 integrating mathematical algorithms (algorithm libraries, mathematical software systems) into LLMs (and vice versa),
 math education supported by combined SC and ML methods,
 collaborative math writing supported by LLMs,
 SC inside training algorithms, e.g. symbolic regression and
fitting (learning appropriate formal expressions for modeling labeled data),
 symbolic data augmentation to generate new training data,
 incorporating symbolic constraints (expressing known logical or physical rules) into ML training,
 explaining ML models by symbolic expressions that (hopefully) express structure in the
domains modeled (“explainable AI”),
 using ML for complexity formulae for hard SC problems.
If you are interested in attending ACA 2024 and in proposing a talk at this session, please send an abstract of your talk to Bo Huang. Please use this LaTeX template for your abstract and send
both the LaTeX source and a compiled PDF version. We suggest that abstracts be no more than 2 pages including references.
Talks
Important Dates
Submission of talks: May 15, 2024
Registration notification: June 1, 2024 (all registration is onsite)
Go to:
ACA 2024 main page
Conferences on Applications of Computer Algebra main page
