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The regularized tau estimator: A robust and efficient solution to ill-posed linear inverse problems

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The reconstruction of the angular and spatial intensity distribution from radiative flux maps measured in high flux solar simulators or optical concentrators is an ill-posed inverse problem requiring special solution strategies. We aimed at providing a sol ...

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PERGAMON-ELSEVIER SCIENCE LTD2018

Max-stable processes are increasingly widely used for modelling complex extreme events, but existing fitting methods are computationally demanding, limiting applications to a few dozen variables. r-Pareto processes are mathematically simpler and have the p ...

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Learning Proximal Operators with Gaussian Processes

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Several distributed-optimization setups involve a group of agents coordinated by a central entity (coordinator), altogether operating in a collaborative framework. In such environments, it is often common that the agents solve proximal minimization problem ...

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Optimization Of Compound Regularization Parameters Based On Stein'S Unbiased Risk Estimate

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Ieee2017

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Gaussian scale mixtures are constructed as Gaussian processes with a random variance. They have non-Gaussian marginals and can exhibit asymptotic dependence unlike Gaussian processes, which are asymptotically independent except in the case of perfect depen ...

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Targeting the problem of generating high-resolution air quality maps for cities, we leverage four different sources of data: (i) in-situ air quality measurements produced by our mobile sensor network deployed on public transportation vehicles, (ii) explana ...

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Proximal Multitask Learning Over Networks With Sparsity-Inducing Coregularization

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In this work, we consider multitask learning problems where clusters of nodes are interested in estimating their own parameter vector. Cooperation among clusters is beneficial when the optimal models of adjacent clusters have a good number of similar entri ...

IEEE2016

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Solving a linear inverse problem may include difficulties such as the presence of outliers and a mixing matrix with a large condition number. In such cases a regularized robust estimator is needed. We propose a new tau-type regularized robust estimator tha ...

2015