Publications by Luca Baldassarre

A Primal-dual Framework For Mixtures Of Regularisers

Volkan Cevher, Cosimo Aprile, Quoc Tran Dinh, Luca Baldassarre, Baran Gözcü

Effectively solving many inverse problems in engineering requires to leverage all possible prior information about the structure of the signal to be estimated. This often leads to tackling constrained optimization problems with mixtures of regularizers. Pr ...

2015

Structured Sampling and Recovery of iEEG Signals

Volkan Cevher, Mahsa Shoaran, Cosimo Aprile, Yusuf Leblebici, Luca Baldassarre

Wireless implantable devices capable of monitoring the electrical activity of the brain are becoming an important tool for understanding, and potentially treating, mental diseases such as epilepsy and depression. Compressive sensing (CS) is emerging as a p ...

2015

Model-based Sketching and Recovery with Expanders

Volkan Cevher, Luca Baldassarre, Bubacarr Bah

Linear sketching and recovery of sparse vectors with randomly constructed sparse matrices has numerous applications in several areas, including compressive sensing, data stream computing, graph sketching, and combinatorial group testing. This paper conside ...

2014

MAP Estimation for Bayesian Mixture Models with Submodular Priors

Volkan Cevher, Luca Baldassarre, Marwa El Halabi

We propose a Bayesian approach where the signal structure can be represented by a mixture model with a submodular prior. We consider an observation model that leads to Lipschitz functions. Due to its combinatorial nature, computing the maximum a posteriori ...

2014

Tractability of interpretability via selection of group-sparse models

Volkan Cevher, Luca Baldassarre, Nirav Bhan

Group-based sparsity models are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing. A promise of these models is to lead to “interpretable” signals for which we identify i ...

2013

To Convexify or Not? Regression with Clustering Penalties on Graphs

Volkan Cevher, Luca Baldassarre, Marwa El Halabi

We consider minimization problems that are compositions of convex functions of a vector

\x \in\reals^N

with submodular set functions of its support (i.e., indices of the non-zero coefficients of

\x

). Such problems are in general difficult for large

N

...

IEEE2013

Accelerated And Inexact Forward-Backward Algorithms

Luca Baldassarre

We propose a convergence analysis of accelerated forward-backward splitting methods for composite function minimization, when the proximity operator is not available in closed form, and can only be computed up to a certain precision. We prove that the 1/k( ...

Siam Publications2013

Convexity in source separation: Models, geometry, and algorithms

Volkan Cevher, Afsaneh Asaei, Quoc Tran Dinh, Luca Baldassarre

Source separation, or demixing, is the process of extracting multiple components entangled within a signal. Contemporary signal processing presents a host of difficult source separation problems, from interference cancellation to background subtraction, bl ...

2013

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A Primal-dual Framework For Mixtures Of Regularisers

Structured Sampling and Recovery of iEEG Signals

Model-based Sketching and Recovery with Expanders

MAP Estimation for Bayesian Mixture Models with Submodular Priors

Tractability of interpretability via selection of group-sparse models

To Convexify or Not? Regression with Clustering Penalties on Graphs

Accelerated And Inexact Forward-Backward Algorithms

Convexity in source separation: Models, geometry, and algorithms

MAP Estimation for Bayesian Mixture Models with Submodular Priors

To Convexify or Not? Regression with Clustering Penalties on Graphs

Structured Sampling and Recovery of iEEG Signals

Tractability of interpretability via selection of group-sparse models

A Primal-dual Framework For Mixtures Of Regularisers

Accelerated And Inexact Forward-Backward Algorithms

Model-based Sketching and Recovery with Expanders

Convexity in source separation: Models, geometry, and algorithms