4 papers
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…
Integral Bayesian symbolic regression for optimal discovery of governing equations from scarce and noisy data
Oriol Cabanas-Tirapu, Sergio Cobo-Lopez, Savannah E. Sanchez +3
Understanding how systems evolve over time often requires discovering the differential equations that govern their behavior. Automatically learning these equations from experimenta…
Bayesian symbolic regression: Automated equation discovery from a physicists' perspective
Roger Guimera, Marta Sales-Pardo
Symbolic regression automates the process of learning closed-form mathematical models from data. Standard approaches to symbolic regression, as well as newer deep learning approach…
A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules
Manuel Ruiz-Botella, Marta Sales-Pardo, Roger GuimerÃ
Developing new molecular compounds is crucial to address pressing challenges, from health to environmental sustainability. However, exploring the molecular space to discover new mo…