1 citations · 2 across the 8 of their papers we have counts for
8 papers
Stellar flare detection in XMM-Newton with gradient boosted trees
Mario Pasquato, Martino Marelli, Andrea De Luca +5
The EXTraS project, based on data collected with the XMM-Newton observatory, provided us with a vast amount of light curves for X-ray sources. For each light curve, EXTraS also pro…
Variable Star Light Curves in Koopman Space
Nicolas Mekhaël, Mario Pasquato, Gaia Carenini +4
We present the first application of data-driven techniques for dynamical system analysis based on Koopman theory to variable stars. We focus on light curves of RRLyrae type variabl…
Interpretable machine learning for finding intermediate-mass black holes
Mario Pasquato, Piero Trevisan, Abbas Askar +4
Definitive evidence that globular clusters (GCs) host intermediate-mass black holes (IMBHs) is elusive. Machine learning (ML) models trained on GC simulations can in principle pred…
Tackling Computational Heterogeneity in FL: A Few Theoretical Insights
Adnan Ben Mansour, Gaia Carenini, Alexandre Duplessis
The future of machine learning lies in moving data collection along with training to the edge. Federated Learning, for short FL, has been recently proposed to achieve this goal. Th…
Sparse logistic regression for RR Lyrae vs binaries classification
Piero Trevisan, Mario Pasquato, Gaia Carenini +4
RR Lyrae (RRL) are old, low-mass radially pulsating variable stars in their core helium burning phase. They are popular stellar tracers and primary distance indicators, since they…
Adversarial Path Planning for Optimal Camera Positioning
Gaia Carenini, Alexandre Duplessis
The use of visual sensors is flourishing, driven among others by the several applications in detection and prevention of crimes or dangerous events. While the problem of optimal ca…
FedControl: When Control Theory Meets Federated Learning
Adnan Ben Mansour, Gaia Carenini, Alexandre Duplessis +1
To date, the most popular federated learning algorithms use coordinate-wise averaging of the model parameters. We depart from this approach by differentiating client contributions…
Federated Learning Aggregation: New Robust Algorithms with Guarantees
Adnan Ben Mansour, Gaia Carenini, Alexandre Duplessis +1
Federated Learning has been recently proposed for distributed model training at the edge. The principle of this approach is to aggregate models learned on distributed clients to ob…