Search Results for author: Matteo Ruffini

Found 6 papers, 2 papers with code

Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling

no code implementations12 May 2022 Alexander Buchholz, Jan Malte Lichtenberg, Giuseppe Di Benedetto, Yannik Stein, Vito Bellini, Matteo Ruffini

When adopting the PL model as a ranking policy, both tasks require the computation of expectations with respect to the model.

Learning-To-Rank

Ranker-agnostic Contextual Position Bias Estimation

no code implementations28 Jul 2021 Oriol Barbany Mayor, Vito Bellini, Alexander Buchholz, Giuseppe Di Benedetto, Diego Marco Granziol, Matteo Ruffini, Yannik Stein

This paper introduces a method for modeling the probability of an item being seen in different contexts, e. g., for different users, with a single estimator.

Learning-To-Rank

Hierarchical Methods of Moments

1 code implementation NeurIPS 2017 Matteo Ruffini, Guillaume Rabusseau, Borja Balle

Spectral methods of moments provide a powerful tool for learning the parameters of latent variable models.

Tensor Decomposition

Generating Synthetic but Plausible Healthcare Record Datasets

no code implementations4 Jul 2018 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.

Clustering Patients with Tensor Decomposition

no code implementations29 Aug 2017 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.

Tensor Decomposition

A New Spectral Method for Latent Variable Models

1 code implementation11 Dec 2016 Matteo Ruffini, Marta Casanellas, Ricard Gavaldà

This paper presents an algorithm for the unsupervised learning of latent variable models from unlabeled sets of data.

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