Symbolic Regression with a Learned Concept Library Omar Costilla-Reyes UT Austin, Foundry Technologies UT Austin MIT Miles Cranmer Swarat Chaudhuri University of Cambridge

We present a novel method for symbolic regression (SR), the task of searching for compact programmatic hypotheses that best explain a dataset. The problem is commonly solved using genetic algorithms; we show that we can enhance such methods by inducing a library of abstract textual concepts.