4k citations · 12.7k across the 44 of their papers we have counts for
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Limits of spectral learning under noise
Sabin Roman, Ljupco Todorovski, Saso Dzeroski +2
Learning functional relationships from noisy data is a central problem in scientific inference. Spectral methods approximate unknown functions by expanding them in a basis and esti…
A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules
Manuel Ruiz-Botella, Marta Sales-Pardo, Roger Guimerà
Developing new molecular compounds is crucial to address pressing challenges, from health to environmental sustainability. However, exploring the molecular space to discover new mo…
Fundamental limits to learning closed-form mathematical models from data
Oscar Fajardo-Fontiveros, Ignasi Reichardt, Harry R. De Los Rios +3
Given a finite and noisy dataset generated with a closed-form mathematical model, when is it possible to learn the true generating model from the data alone? This is the question w…
A Bayesian machine scientist to aid in the solution of challenging scientific problems
Roger Guimera, Ignasi Reichardt, Antoni Aguilar-Mogas +4
Closed-form, interpretable mathematical models have been instrumental for advancing our understanding of the world; with the data revolution, we may now be in a position to uncover…