7 citations · 8 across the 4 of their papers we have counts for
4 papers · 1 filter
Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling
Alexander Buchholz, Jan Malte Lichtenberg, Giuseppe Di Benedetto +3
The Plackett-Luce (PL) model is ubiquitous in learning-to-rank (LTR) because it provides a useful and intuitive probabilistic model for sampling ranked lists. Counterfactual offlin…
Hierarchical Methods of Moments
Matteo Ruffini, Guillaume Rabusseau, Borja Balle
Spectral methods of moments provide a powerful tool for learning the parameters of latent variable models. Despite their theoretical appeal, the applicability of these methods to r…
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…