Marco Virgolin — accepted AI-conference papers

2023

Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search

Pierre-Alexandre Kamienny, Guillaume Lample, sylvain lamprier, Marco Virgolin

ICML 2023poster

Symbolic regression (SR) is the problem of learning a symbolic expression from numerical data. Recently, deep neural models trained on procedurally-generated synthetic datasets showed competitive performance compared to more classical Genetic Programming (GP) ones. Unlike their GP counterparts, thes…

Cited by 30 Source ↗PDF ↗Scholar ↗

2021

Contemporary Symbolic Regression Methods and their Relative Performance

William La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabricio Olivetti de Franca, Marco Virgolin, Ying Jin +2

NeurIPS 2021poster

Many promising approaches to symbolic regression have been presented in recent years, yet progress in the field continues to suffer from a lack of uniform, robust, and transparent benchmarking standards. In this paper, we address this shortcoming by introducing an open-source, reproducible benchmark…

Cited by 418 Source ↗Scholar ↗