Publications related to A robust minimum-variance filter for time varying uncertain discrete-time systems

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

Martin Vetterli, Benjamin Bejar Haro, Marta Martinez-Camara

Linear inverse problems are ubiquitous. Often the measurements do not follow a Gaussian distribution. Additionally, a model matrix with a large condition number can complicate the problem further by making it ill-posed. In this case, the performance of pop ...

2017

Blowing in the Wind

Marta Martinez-Camara

Every day tons of pollutants are emitted into the atmosphere all around the world. These pollutants are altering the equilibrium of our planet, causing profound changes in its climate, increasing global temperatures, and raising the sea level. The need to ...

EPFL2017

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

Xin Liu, Hanjie Pan, Feng Xue, Jiaqi Liu

Recently, the type of compound regularizers has become a popular choice for signal reconstruction. The estimation quality is generally sensitive to the values of multiple regularization parameters. In this work, based on BDF algorithm, we develop a data-dr ...

Ieee2017

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

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

Statistical Unfolding Of Elementary Particle Spectra: Empirical Bayes Estimation And Bias-Corrected Uncertainty Quantification

Victor Panaretos, Mikael Johan Kuusela

We consider the high energy physics unfolding problem where the goal is to estimate the spectrum of elementary particles given observations distorted by the limited resolution of a particle detector. This important statistical inverse problem arising in da ...

Inst Mathematical Statistics2015

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Volkan Cevher, Jonathan Mark Scarlett, Yen-Huan Li

We consider the model selection consistency or sparsistency of a broad set of

\ell_1

-regularized

M

-estimators for linear and non-linear statistical models in a unified fashion. For this purpose, we propose the local structured smoothness condition (LSS ...

2015

Time–Data Tradeoffs by Aggressive Smoothing

Volkan Cevher

This paper proposes a tradeoff between sample complexity and computation time that applies to statistical estimators based on convex optimization. As the amount of data increases, we can smooth optimization problems more and more aggressively to achieve ac ...

2014

Bayesian Estimation for Continuous-Time Sparse Stochastic Processes

Michaël Unser, Arash Amini, Emrah Bostan, Ulugbek Kamilov

We consider continuous-time sparse stochastic processes from which we have only a finite number of noisy/noiseless samples. Our goal is to estimate the noiseless samples (denoising) and the signal in-between (interpolation problem). By relying on tools fro ...

Ieee-Inst Electrical Electronics Engineers Inc2013

A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs

Jean-Marc Odobez, Rémi Emonet, Jagannadan Varadarajan

This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns called motifs from documents given as word×time count matrices (e.g., videos). In this model, documents are represented as a mixture of sequenti ...

Springer2013

A robust minimum-variance filter for time varying uncertain discrete-time systems

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

Blowing in the Wind

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

Robust Distributed Estimation by Networked Agents

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

Statistical Unfolding Of Elementary Particle Spectra: Empirical Bayes Estimation And Bias-Corrected Uncertainty Quantification

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Time–Data Tradeoffs by Aggressive Smoothing

Bayesian Estimation for Continuous-Time Sparse Stochastic Processes

A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs

Sparsistency of $\ell_1$-Regularized $M$-Estimators

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

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

Robust Distributed Estimation by Networked Agents

Blowing in the Wind

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

Time–Data Tradeoffs by Aggressive Smoothing

Statistical Unfolding Of Elementary Particle Spectra: Empirical Bayes Estimation And Bias-Corrected Uncertainty Quantification

Bayesian Estimation for Continuous-Time Sparse Stochastic Processes

A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs