abstract = "Symbolic Regression (SR) searches for a closed-form
mathematical expression describing the relationship
between input and output features in data. The main
theoretical draw of SR compared to traditional
black-box regression techniques is that the learnt
models should be interpretable by design. However,
typical SR methods struggle to discover sparse and
accurate models when the shape of the output varies
locally, depending on the values of some input
features. Given that this is a common occurrence in
physics, SR should be able to learn piecewise models.
We introduce a new piecewise SR framework called
Unified Piecewise Symbolic Regression (UPSR). UPSR
simultaneously partitions the input space into
subregions and learns local regressors for each
subregion, forming a global model unifying all
subregions. We demonstrate its effectiveness on a large
synthetic SR benchmark containing both piecewise and
non-piecewise data structures. UPSR is shown to
outperform state-of-the-art piecewise SR approaches,
both qualitatively and quantitatively.",
notes = "also known as \cite{paper_26_unified_piecewise}
Part of \cite{Xue:2025:GP} EuroGP'2025 held in
conjunction with EvoCOP2025, EvoMusArt2025 and
EvoApplications2025",
