Publications related to Nonideal sampling and regularized interpolation of noisy data

Jump-Penalized Least Absolute Values Estimation of Scalar or Circle-Valued Signals

We study jump-penalized estimators based on least absolute deviations which are often referred to as Potts estimators. They are estimators for a parsimonious piecewise constant representation of noisy data having a noise distribution which has heavier tail ...

Oxford University Press2017

Robust Distributed Estimation by Networked Agents

Ali H. Sayed, Ali Zoubir

Diffusion adaptive networks tasked with solving estimation problems have attracted attention in recent years due to their reliability, scalability, resource efficiency, and resilience to node and link failure. Diffusion adaptation strategies that are based ...

2017

Acoustic DoA Estimation by One Unsophisticated Sensor

Martin Vetterli, Ivan Dokmanic, Dalia Salem Hassan Fahmy El Badawy

We show how introducing known scattering can be used in direction of arrival estimation by a single sensor. We first present an analysis of the geometry of the underlying measurement space and show how it enables localizing white sources. Then, we extend t ...

Springer Verlag2017

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Emrah Bostan

We propose new regularization models to solve inverse problems encountered in biomedical imaging applications. In formulating mathematical schemes, we base our approach on the sparse signal processing principles that have emerged as a central paradigm in t ...

EPFL2016

MMSE Denoising of Sparse and Non-Gaussian AR(1) Processes

Michaël Unser, Emrah Bostan, Pedram Pad, Pouria Tohidi

We propose two minimum-mean-square-error (MMSE) estimation methods for denoising non-Gaussian first-order autoregressive (AR(1)) processes. The first one is based on the message passing framework and gives the exact theoretic MMSE estimator. The second is ...

IEEE2016

Comparing molecules and solids across structural and alchemical space

Michele Ceriotti, Sandip De

Evaluating the (dis)similarity of crystalline, disordered and molecular compounds is a critical step in the development of algorithms to navigate automatically the configuration space of complex materials. For instance, a structural similarity metric is cr ...

Royal Society of Chemistry2016

Proximal Multitask Learning Over Networks With Sparsity-Inducing Coregularization

Ali H. Sayed, Roula Nassif

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

Importance Sampling Tree for Large-scale Empirical Expectation

François Fleuret, Olivier Canévet, Cijo Jose

We propose a tree-based procedure inspired by the Monte-Carlo Tree Search that dynamically modulates an importance-based sampling to prioritize computation, while getting unbiased estimates of weighted sums. We apply this generic method to learning on very ...

2016

Estimation Error of the Constrained Lasso

Volkan Cevher, Yen-Huan Li, Ya-Ping Hsieh, Nissim Zerbib

This paper presents a non-asymptotic upper bound for the estimation error of the constrained lasso, under the high-dimensional (

n \ll p

) setting. In contrast to existing results, the error bound in this paper is sharp, is valid when the parameter to be e ...

2016

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

Volkan Cevher

This paper proposes a tradeoff between computational time, sample complexity, and statistical accuracy that applies to statistical estimators based on convex optimization. When we have a large amount of data, we can exploit excess samples to decrease stati ...

Ieee-Inst Electrical Electronics Engineers Inc2015

Nonideal sampling and regularized interpolation of noisy data

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Jump-Penalized Least Absolute Values Estimation of Scalar or Circle-Valued Signals

Robust Distributed Estimation by Networked Agents

Acoustic DoA Estimation by One Unsophisticated Sensor

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

MMSE Denoising of Sparse and Non-Gaussian AR(1) Processes

Comparing molecules and solids across structural and alchemical space

Proximal Multitask Learning Over Networks With Sparsity-Inducing Coregularization

Importance Sampling Tree for Large-scale Empirical Expectation

Estimation Error of the Constrained Lasso

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Proximal Multitask Learning Over Networks With Sparsity-Inducing Coregularization

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

Robust Distributed Estimation by Networked Agents

Jump-Penalized Least Absolute Values Estimation of Scalar or Circle-Valued Signals

Acoustic DoA Estimation by One Unsophisticated Sensor

MMSE Denoising of Sparse and Non-Gaussian AR(1) Processes

Estimation Error of the Constrained Lasso

Importance Sampling Tree for Large-scale Empirical Expectation

Comparing molecules and solids across structural and alchemical space