5 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…
Gender and the influence of research environment in topic selection of early-career faculty in STEM
Lluis Danus, Robert H. Davis, Roger Guimera +1
We study the influence that research environments have in shaping careers of early-career faculty in terms of their research portfolio. We find that departments exert an attractive…