Publications related to A Simple Procedure to Extract Speed Distributions from Ion Images with a Large Background Contribution

Regularization for distributionally robust state estimation and prediction

Giancarlo Ferrari Trecate, Jean-Sébastien Hubert Brouillon, Florian Dörfler

The increasing availability of sensing techniques provides a great opportunity for engineers to design state estimation methods, which are optimal for the system under observation and the observed noise patterns. However, these patterns often do not fulfil ...

2023

Wasserstein Distributionally Robust Learning

Soroosh Shafieezadeh Abadeh

Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...

EPFL2020

Robust T2 Relaxometry with Hamiltonian MCMC for Myelin Water Fraction Estimation

Jean-Philippe Thiran, Thomas Yu, Marco Pizzolato

We present a voxel-wise Bayesian multi-compartment T2 relaxometry fitting method based on Hamiltonian Markov Chain Monte Carlo (HMCMC) sampling. The T 2 spectrum is modeled as a mixture of truncated Gaussian components, which involves the estimation of par ...

IEEE2019

Wasserstein Distributionally Robust Kalman Filtering

Daniel Kuhn, Viet Anh Nguyen, Soroosh Shafieezadeh Abadeh, Peyman Mohajerin Esfahani

We study a distributionally robust mean square error estimation problem over a nonconvex Wasserstein ambiguity set containing only normal distributions. We show that the optimal estimator and the least favorable distribution form a Nash equilibrium. Despit ...

2018

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

Martin Vetterli, Marta Martinez-Camara, Benjamin Bejar Haro

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

Guided Mesh Normal Filtering

Sofien Bouaziz, Bailin Deng

The joint bilateral filter is a variant of the standard bilateral filter, where the range kernel is evaluated using a guidance signal instead of the original signal. It has been successfully applied to various image processing problems, where it provides m ...

Wiley-Blackwell2015

Adaptive Image Resizing Based on Continuous-Domain Stochastic Modeling

Michaël Unser, John Paul Ward, Aurélien Bourquard, Hagay Kirshner

We introduce an adaptive continuous-domain modeling approach to texture and natural images. The continuous-domain image is assumed to be a smooth function, and we embed it in a parameterized Sobolev space. We point out a link between Sobolev spaces and sto ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Stratégies de compromis pour l'estimation des paramètres de régression et pour la classification floue

Nicolas Fournier

Model specification is an integral part of any statistical inference problem. Several model selection techniques have been developed in order to determine which model is the best one among a list of possible candidates. Another way to deal with this questi ...

EPFL2011

Optimal Conditionally Unbiased Bounded-Influence Inference in Dynamic Location and Scale Models

Loriano Mancini, Fabio Trojani

This paper studies the local robustness of estimators and tests for the conditional location and scale parameters in a strictly stationary time series model. We first derive optimal bounded-influence estimators for such settings under a conditionally Gauss ...

2005