144 citations · 367 across the 18 of their papers we have counts for
4 papers · 1 filter
Genetic Programming is Naturally Suited to Evolve Bagging Ensembles
Marco Virgolin
Learning ensembles by bagging can substantially improve the generalization performance of low-bias, high-variance estimators, including those evolved by Genetic Programming (GP). T…
Learning a Formula of Interpretability to Learn Interpretable Formulas
Marco Virgolin, Andrea De Lorenzo, Eric Medvet +1
Many risk-sensitive applications require Machine Learning (ML) models to be interpretable. Attempts to obtain interpretable models typically rely on tuning, by trial-and-error, hyp…
Local Search is a Remarkably Strong Baseline for Neural Architecture Search
T. Den Ottelander, A. Dushatskiy, M. Virgolin +1
Neural Architecture Search (NAS), i.e., the automation of neural network design, has gained much popularity in recent years with increasingly complex search algorithms being propos…
Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy
M. Virgolin, Z. Wang, B. V. Balgobind +11
To study radiotherapy-related adverse effects, detailed dose information (3D distribution) is needed for accurate dose-effect modeling. For childhood cancer survivors who underwent…