Publications by Luca Baldassarre

Adaptive Learning-Based Compressive Sampling for Low-power Wireless Implants

Catherine Dehollain, Yusuf Leblebici, Volkan Cevher, Mahsa Shoaran, Gürkan Yilmaz, Franco Maloberti, Cosimo Aprile, Kerim Türe, Luca Baldassarre

Implantable systems are nowadays being used to interface the human brain with external devices, in order to understand and potentially treat neurological disorders. The most predominant design constraints are the system’s area and power. In this paper, we ...

2018

An area and power efficient on-the-fly LBCS transformation for implantable neuronal signal acquisition systems

Yusuf Leblebici, Volkan Cevher, Cosimo Aprile, Luca Baldassarre

A power and area effcient hardware encoding system tailored for wireless implantable applications is presented. Constant medical monitoring allowed by implantable devices is the most relevant alternative to current bulky monitoring systems, which, in case ...

2018

An area and power efficient on-the-fly LBCS transformation for implantable neuronal signal acquisition systems

Yusuf Leblebici, Volkan Cevher, Cosimo Aprile, Luca Baldassarre

A power and area efficient hardware encoding system tailored for wireless implantable applications is presented. Constant medical monitoring allowed by implantable devices is the most relevant alternative to current bulky monitoring systems, which, in case ...

ASSOC COMPUTING MACHINERY2018

Learning-based subsampling

Volkan Cevher, Ilija Bogunovic, Baran Gözcü, Yen-Huan Li, Luca Baldassarre, Jonathan Mark Scarlett

The present invention concerns a method of sampling a test signal. The method comprises: acquiring (21) training signals sampled at a plurality of sampling locations; running (23) an optimization procedure for determining an index set of n indices, represe ...

2017

DCT Learning-Based Hardware Design for Neural Signal Acquisition Systems

Yusuf Leblebici, Volkan Cevher, Cosimo Aprile, Luca Baldassarre

This work presents an area and power efficient encoding system for wireless implantable devices capable of monitoring the electrical activity of the brain. Such devices are becoming an important tool for understanding, real-time monitoring, and potentially ...

2017

Group-Sparse Model Selection: Hardness and Relaxations

Volkan Cevher, Anastasios Kyrillidis, 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. The main promise of these models is the recovery of ``interpretable" signals along with ...

Institute of Electrical and Electronics Engineers2016

Convex block-sparse linear regression with expanders - provably

Volkan Cevher, Anastasios Kyrillidis, Luca Baldassarre, Bubacarr Bah, Quoc Tran Dinh

Sparse matrices are favorable objects in machine learning and optimization. When such matrices are used, in place of dense ones, the overall complexity requirements in optimization can be significantly reduced in practice, both in terms of space and run-ti ...

2016

A Primal-dual Framework For Mixtures Of Regularisers

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

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

Sparse Group Covers and Greedy Tree Approximations

Volkan Cevher, Luca Baldassarre

We consider the problem of finding a K-sparse approximation of a signal, such that the support of the approximation is the union of sets from a given collection, a.k.a. group structure. This problem subsumes the one of finding K-sparse tree approximations. ...

2015

Structured Sampling and Recovery of iEEG Signals

Yusuf Leblebici, Volkan Cevher, Mahsa Shoaran, Cosimo Aprile, 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, Marwa El Halabi, Luca Baldassarre

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

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

To Convexify or Not? Regression with Clustering Penalties on Graphs

Volkan Cevher, Marwa El Halabi, Luca Baldassarre

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

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