50 citations · 139 across the 24 of their papers we have counts for
25 papers
Sparse learning with concave regularization: relaxation of the irrepresentable condition
V. Cerone, S. M. Fosson, D. Regruto +1
Learning sparse models from data is an important task in all those frameworks where relevant information should be identified within a large dataset. This can be achieved by formul…
Alternating direction method of multipliers for polynomial optimization
V. Cerone, S. M. Fosson, S. Pirrera +1
Multivariate polynomial optimization is a prevalent model for a number of engineering problems. From a mathematical viewpoint, polynomial optimization is challenging because it is…
Fast sparse optimization via adaptive shrinkage
Vito Cerone, Sophie M. Fosson, Diego Regruto
The need for fast sparse optimization is emerging, e.g., to deal with large-dimensional data-driven problems and to track time-varying systems. In the framework of linear sparse op…
Lasso-based state estimation for cyber-physical systems under sensor attacks
Vito Cerone, Sophie M. Fosson, Diego Regruto +1
The development of algorithms for secure state estimation in vulnerable cyber-physical systems has been gaining attention in the last years. A consolidated assumption is that an ad…
A convex optimization approach to online set-membership EIV identification of LTV systems
Sophie M. Fosson, Diego Regruto, Talal Abdalla +1
This paper addresses the problem of recursive set-membership identification for linear time varying (LTV) systems when both input and output measurements are affected by bounded ad…
Binary input reconstruction for linear systems: a performance analysis
Sophie M. Fosson
Recovering the digital input of a time-discrete linear system from its (noisy) output is a significant challenge in the fields of data transmission, deconvolution, channel equaliza…
Sparse linear regression from perturbed data
S. M. Fosson, V. Cerone, D. Regruto
The problem of sparse linear regression is relevant in the context of linear system identification from large datasets. When data are collected from real-world experiments, measure…
Enhancing low-rank solutions in semidefinite relaxations of Boolean quadratic problems
V. Cerone, S. M. Fosson, D. Regruto
Boolean quadratic optimization problems occur in a number of applications. Their mixed integer-continuous nature is challenging, since it is inherently NP-hard. For this motivation…
Centralized and distributed online learning for sparse time-varying optimization
Sophie M. Fosson
The development of online algorithms to track time-varying systems has drawn a lot of attention in the last years, in particular in the framework of online convex optimization. Mea…
Sparse linear regression with compressed and low-precision data via concave quadratic programming
Vito Cerone, Sophie M. Fosson, Diego Regruto
We consider the problem of the recovery of a k-sparse vector from compressed linear measurements when data are corrupted by a quantization noise. When the number of measurements is…
Analysis of SparseHash: an efficient embedding of set-similarity via sparse projections
Diego Valsesia, Sophie Marie Fosson, Chiara Ravazzi +2
Embeddings provide compact representations of signals in order to perform efficient inference in a wide variety of tasks. In particular, random projections are common tools to cons…
Recovery of binary sparse signals from compressed linear measurements via polynomial optimization
Sophie M. Fosson, Mohammad Abuabiah
The recovery of signals with finite-valued components from few linear measurements is a problem with widespread applications and interesting mathematical characteristics. In the co…
A linear programming approach to sparse linear regression with quantized data
Vito Cerone, Sophie M. Fosson, Diego Regruto
The sparse linear regression problem is difficult to handle with usual sparse optimization models when both predictors and measurements are either quantized or represented in low-p…
A biconvex analysis for Lasso l1 reweighting
Sophie M. Fosson
l1 reweighting algorithms are very popular in sparse signal recovery and compressed sensing, since in the practice they have been observed to outperform classical l1 methods. Never…
Non-convex Lasso-kind approach to compressed sensing for finite-valued signals
Sophie M. Fosson
In this paper, we bring together two trends that have recently emerged in sparse signal recovery: the problem of sparse signals that stem from finite alphabets and the techniques t…
Non-convex approach to binary compressed sensing
Sophie M. Fosson
We propose a new approach to the recovery of binary signals in compressed sensing, based on the local minimization of a non-convex cost functional. The desired signal is proved to…
Online optimization in dynamic environments: a regret analysis for sparse problems
Sophie M. Fosson
Time-varying systems are a challenge in many scientific and engineering areas. Usually, estimation of time-varying parameters or signals must be performed online, which calls for t…
GPU-Accelerated Algorithms for Compressed Signals Recovery with Application to Astronomical Imagery Deblurring
Attilio Fiandrotti, Sophie M. Fosson, Chiara Ravazzi +1
Compressive sensing promises to enable bandwidth-efficient on-board compression of astronomical data by lifting the encoding complexity from the source to the receiver. The signal…
Distributed recovery of jointly sparse signals under communication constraints
Sophie M. Fosson, Javier Matamoros, Carles Anton-Haro +1
The problem of the distributed recovery of jointly sparse signals has attracted much attention recently. Let us assume that the nodes of a network observe different sparse signals…
Steerable Discrete Cosine Transform
Giulia Fracastoro, Sophie Marie Fosson, Enrico Magli
In image compression, classical block-based separable transforms tend to be inefficient when image blocks contain arbitrarily shaped discontinuities. For this reason, transforms in…
Voyager 2 solar plasma and magnetic field spectral analysis for intermediate data sparsity
Luca Gallana, Federico Fraternale, Michele Iovieno +5
The Voyager probes are the furthest, still active, spacecraft ever launched from Earth. During their 38-year trip, they have collected data regarding solar wind properties (such as…
Distributed soft thresholding for sparse signal recovery
Chiara Ravazzi, Sophie M. Fosson, Enrico Magli
In this paper, we address the problem of distributed sparse recovery of signals acquired via compressed measurements in a sensor network. We propose a new class of distributed algo…
A distributed classification/estimation algorithm for sensor networks
Fabio Fagnani, Sophie M. Fosson, Chiara Ravazzi
In this paper, we address the problem of simultaneous classification and estimation of hidden parameters in a sensor network with communications constraints. In particular, we cons…
A Decoding Approach to Fault Tolerant Control of Linear Systems with Quantized Disturbance Input
Sophie M. Fosson
The aim of this paper is to propose an alternative method to solve a Fault Tolerant Control problem. The model is a linear system affected by a disturbance term: this represents a…