3 citations · 7 across the 5 of their papers we have counts for
5 papers · 1 filter
ERBench: A Benchmark and Testsuite for Equation Discovery Algorithms
Paul Kahlmeyer, Henrik Voigt, Michael Habeck +1
Equation discovery aims to automate the discovery of scientific models in the form of mathematical equations from data. Technically, equation discovery is implemented by symbolic r…
Analyzing Generalization in Pre-Trained Symbolic Regression
Henrik Voigt, Paul Kahlmeyer, Kai Lawonn +2
Symbolic regression algorithms search a space of mathematical expressions for formulas that explain given data. Transformer-based models have emerged as a promising, scalable appro…
Scaling Up Unbiased Search-based Symbolic Regression
Paul Kahlmeyer, Joachim Giesen, Michael Habeck +1
In a regression task, a function is learned from labeled data to predict the labels at new data points. The goal is to achieve small prediction errors. In symbolic regression, the…
Discovering Symmetries of ODEs by Symbolic Regression
Paul Kahlmeyer, Niklas Merk, Joachim Giesen
Solving systems of ordinary differential equations (ODEs) is essential when it comes to understanding the behavior of dynamical systems. Yet, automated solving remains challenging,…
Dimension Reduction for Symbolic Regression
Paul Kahlmeyer, Markus Fischer, Joachim Giesen
Solutions of symbolic regression problems are expressions that are composed of input variables and operators from a finite set of function symbols. One measure for evaluating symbo…