no code implementations • 9 Mar 2023 • Gaia Grosso, Nicolò Lai, Marco Letizia, Jacopo Pazzini, Marco Rando, Lorenzo Rosasco, Andrea Wulzer, Marco Zanetti
We here propose a machine learning approach for monitoring particle detectors in real-time.
no code implementations • 14 Sep 2022 • Paolo Didier Alfano, Marco Rando, Marco Letizia, Francesca Odone, Lorenzo Rosasco, Vito Paolo Pastore
We compare our method with state-of-the-art unsupervised approaches, where a set of pre-defined hand-crafted features is used for clustering of plankton images.
no code implementations • 10 Jun 2022 • Marco Rando, Cesare Molinari, Silvia Villa, Lorenzo Rosasco
For smooth convex functions we prove almost sure convergence of the iterates and a convergence rate on the function values of the form $O(d/l k^{-c})$ for every $c<1/2$, which is arbitrarily close to the one of Stochastic Gradient Descent (SGD) in terms of number of iterations.
no code implementations • 5 Apr 2022 • Marco Letizia, Gianvito Losapio, Marco Rando, Gaia Grosso, Andrea Wulzer, Maurizio Pierini, Marco Zanetti, Lorenzo Rosasco
We present a machine learning approach for model-independent new physics searches.
no code implementations • 16 Jun 2021 • Marco Rando, Luigi Carratino, Silvia Villa, Lorenzo Rosasco
In this paper, we introduce Ada-BKB (Adaptive Budgeted Kernelized Bandit), a no-regret Gaussian process optimization algorithm for functions on continuous domains, that provably runs in $O(T^2 d_\text{eff}^2)$, where $d_\text{eff}$ is the effective dimension of the explored space, and which is typically much smaller than $T$.