Probabilistic Symbolic Regression for Equation Discovery via Operator-induced and Regularized Symbolic Forests
This work introduces a probabilistic symbolic regression framework that represents mathematical expressions as ensembles of symbolic trees, and develops posterior concentration guarantees when symbolic expressions approximate the underlying relationship arbitrarily well, with a near-parametric rate when an exact finite formula exists.
Somjit Roy, Pritam Dey, B. Mallick et al.
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