3 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Beyond the Training Distribution: Mapping Generalization Boundaries in Neural Program Synthesis
Henrik Voigt, Michael Habeck, Joachim Giesen
Large-scale transformers achieve impressive results on program synthesis benchmarks, yet their true generalization capabilities remain obscured by data contamination and opaque tra…
cs.LG2025
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…
cs.LG2025★ 3 cited
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…