MathematicsNature Communications
Fundamental limits to learning closed-form mathematical models from data
O. Fajardo-fontiveros, I. Reichardt, et al.
This research by Oscar Fajardo-Fontiveros, Ignasi Reichardt, Harry R. De Los Ríos, Jordi Duch, Marta Sales-Pardo, and Roger Guimerà uncovers groundbreaking insights into the challenges of learning mathematical models from noisy data. Discover the pivotal phase transition that determines whether models can be learned effectively or not, along with the innovative use of probabilistic model selection.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Medicine and Health
Deep learning from "passive feeding" to "selective eating" of real-world data
Z. Li, C. Guo, et al.
Medicine and Health
A machine learning approach predicts future risk to suicidal ideation from social media data
A. Roy, K. Nikolitch, et al.
Psychology
Constructing neural network models from brain data reveals representational transformations linked to adaptive behavior
T. Ito, G. R. Yang, et al.
Computer Science
Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing
D. Rankin, M. Black, et al.

