25 citations · 35 across the 5 of their papers we have counts for
5 papers
Generating Synthetic but Plausible Healthcare Record Datasets
Laura Aviñó, Matteo Ruffini, Ricard Gavaldà
Generating datasets that "look like" given real ones is an interesting tasks for healthcare applications of ML and many other fields of science and engineering. In this paper we pr…
Clustering Patients with Tensor Decomposition
Matteo Ruffini, Ricard Gavaldà, Esther Limón
In this paper we present a method for the unsupervised clustering of high-dimensional binary data, with a special focus on electronic healthcare records. We present a robust and ef…
A New Spectral Method for Latent Variable Models
Matteo Ruffini, Marta Casanellas, Ricard Gavaldà
This paper presents an algorithm for the unsupervised learning of latent variable models from unlabeled sets of data. We base our technique on spectral decomposition, providing a t…
Resource-bounded Dimension in Computational Learning Theory
Ricard Gavalda, Maria Lopez-Valdes, Elvira Mayordomo +1
This paper focuses on the relation between computational learning theory and resource-bounded dimension. We intend to establish close connections between the learnability/nonlearna…
Practical algorithms for on-line sampling
Carlos Domingo, Ricard Gavalda, Osamu Watanabe
One of the core applications of machine learning to knowledge discovery consists on building a function (a hypothesis) from a given amount of data (for instance a decision tree or…