23 citations · 26 across the 9 of their papers we have counts for
9 papers
Learning Dynamical Systems from Multiple Sparse Datasets: A Hierarchical Bayesian Modeling Approach
Cristian Brugnara, Lea Multerer, Marco Forgione +1
Estimating parameters of dynamical systems from sparse, noisy, and irregularly sampled data is often severely ill-conditioned. When multiple related datasets are available, they pr…
FPLIER: Federated Pathway-Level Information Extractor
Daniele Malpetti, Christian Berchtold, Francesco Gualdi +3
In transcriptomics, gene-set-aware factorization methods such as the Pathway Level Information Extractor (PLIER) are most effective when trained on large, heterogeneous expression…
A reduced model for the long-term effects of physical activity on type 2 diabetes
Lea Multerer, Pierluigi Francesco De Paola, Marta Lenatti +2
Type 2 diabetes progresses slowly and may be reversed through lifestyle changes, but quantifying the long-term impact of regular physical activity remains challenging due to sparse…
Flotta: a Secure and Flexible Spark-inspired Federated Learning Framework
Claudio Bonesana, Daniele Malpetti, Sandra Mitrović +2
We present Flotta, a Federated Learning framework designed to train machine learning models on sensitive data distributed across a multi-party consortium conducting research in con…
Global Outlier Detection in a Federated Learning Setting with Isolation Forest
Daniele Malpetti, Laura Azzimonti
We present a novel strategy for detecting global outliers in a federated learning setting, targeting in particular cross-silo scenarios. Our approach involves the use of two server…
Using Mixed-Effects Models to Learn Bayesian Networks from Related Data Sets
Marco Scutari, Christopher Marquis, Laura Azzimonti
We commonly assume that data are a homogeneous set of observations when learning the structure of Bayesian networks. However, they often comprise different data sets that are relat…
An exact kernel framework for spatio-temporal dynamics
Oleg Szehr, Dario Azzimonti, Laura Azzimonti
A kernel-based framework for spatio-temporal data analysis is introduced that applies in situations when the underlying system dynamics are governed by a dynamic equation. The key…
A Bayesian Hierarchical Score for Structure Learning from Related Data Sets
Laura Azzimonti, Giorgio Corani, Marco Scutari
Score functions for learning the structure of Bayesian networks in the literature assume that data are a homogeneous set of observations; whereas it is often the case that they com…
Hierarchical Multinomial-Dirichlet model for the estimation of conditional probability tables
L. Azzimonti, G. Corani, M. Zaffalon
We present a novel approach for estimating conditional probability tables, based on a joint, rather than independent, estimate of the conditional distributions belonging to the sam…