no code implementations • 15 Oct 2022 • Alexander Buchholz, Vito Bellini, Giuseppe Di Benedetto, Yannik Stein, Matteo Ruffini, Fabian Moerchen
We suggest an approach to measure and disentangle the effect of simultaneous experiments by providing a cost sharing approach based on Shapley values.
no code implementations • 12 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.
no code implementations • 28 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.
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.
no code implementations • 4 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.
no code implementations • 29 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.
1 code implementation • 11 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.