Publications associées à L-estimator

A Method of Moments Estimator for Interacting Particle Systems and their Mean Field Limit

We study the problem of learning unknown parameters in stochastic interacting particle systems with polynomial drift, interaction, and diffusion functions from the path of one single particle in the system. Our estimator is obtained by solving a linear sys ...

Siam Publications2024

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

Euclid preparation: XXVIII. Forecasts for ten different higher-order weak lensing statistics

Frédéric Courbin, Gianluca Castignani, Austin Chandler Peel, Jean-Luc Starck, Maurizio Martinelli, Yi Wang, Richard Massey, Fabio Finelli, Marcello Farina

Recent cosmic shear studies have shown that higher-order statistics (HOS) developed by independent teams now outperform standard two-point estimators in terms of statistical precision thanks to their sensitivity to the non-Gaussian features of large-scale ...

EDP SCIENCES S A2023

Quantifying uncertain system outputs via the multi-level Monte Carlo method --- distribution and robustness measures

Fabio Nobile, Sebastian Krumscheid, Sundar Subramaniam Ganesh

In this work, we consider the problem of estimating the probability distribution, the quantile or the conditional expectation above the quantile, the so called conditional-value-at-risk, of output quantities of complex random differential models by the MLM ...

2023

3-Dimensional Fluid and White Matter Suppression Magnetic Resonance Imaging Sequence Accelerated With Compressed Sensing Improves Multiple Sclerosis Cervical Spinal Cord Lesion Detection Compared With Standard 2-Dimensional Imaging

Tobias Kober

Objectives Fluid and white matter suppression (FLAWS) is a recently proposed magnetic resonance sequence derived from magnetization-prepared 2 rapid acquisition gradient-echo providing 2 coregistered datasets with white matter- and cerebrospinal fluid-supp ...

LIPPINCOTT WILLIAMS & WILKINS2022

Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts

Daniel Kuhn, Viet Anh Nguyen, Bahar Taskesen

Least squares estimators, when trained on a few target domain samples, may predict poorly. Supervised domain adaptation aims to improve the predictive accuracy by exploiting additional labeled training samples from a source distribution that is close to th ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Lung Ultrasonography for Risk Stratification in Patients with Coronavirus Disease 2019 (COVID-19): A Prospective Observational Cohort Study

Mary-Anne Hartley, Jean-Yves Meuwly

Background. Lung ultrasonography (LUS) is a promising pragmatic risk-stratification tool in coronavirus disease 2019 (COVID-19). This study describes and compares LUS characteristics between patients with different clinical outcomes. ...

OXFORD UNIV PRESS INC2021

Central limit theorems for multilevel Monte Carlo methods

Sebastian Krumscheid, Hakon Andreas Hoel

In this work, we show that uniform integrability is not a necessary condition for central limit theorems (CLT) to hold for normalized multilevel Monte Carlo (MLMC) estimators and we provide near optimal weaker conditions under which the CLT is achieved. In ...

2019

An Easily Computable Error Estimator In Space And Time For The Wave Equation

Marco Picasso, Olga Gorynina

We propose a cheaper version of a posteriori error estimator from Gorynina et al. (Namer. Anal. (2017)) for the linear second-order wave equation discretized by the Newmark scheme in time and by the finite element method in space. The new estimator preserv ...

EDP Sciences2019

Hybrid regularisation and the (in)admissibility of ridge regression in infinite dimensional Hilbert spaces

Victor Panaretos, Anirvan Chakraborty

We consider the problem of estimating the slope function in a functional regression with a scalar response and a functional covariate. This central problem of functional data analysis is well known to be ill-posed, thus requiring a regularised estimation p ...

2019

Tightly Coupled GNSS/INS Integration based on Robust M-Estimators

Jan Skaloud, Michael Meurer

The combination of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS) has become the baseline of many vehicular applications. However, in challenging GNSS scenarios, classical GNSS/INS integration estimators are very sensitive ...

2018

An order-statistics-inspired, fully-digital readout approach for analog SiPM arrays

Edoardo Charbon, Esteban Venialgo Araujo

In this paper we present a silicon photomultiplier (SiPM) readout system based on a field-programmable gate array (FPGA), which is capable of converting any commercial 16-channel analog SiPM array into a hybrid device with fully-digital readout for applica ...

2018

Dynamic pressure probe response tests for robust measurements in periodic flows close to probe resonating frequency

Jürg Alexander Schiffmann, Fatma Ceyhun Sahin

A single-hole probe was designed to measure steady and periodic flows with high fluctuation amplitudes and with minimal flow intrusion. Because of its high aspect ratio, estimations showed that the probe resonates at a frequency two orders of magnitude low ...

Institute of Physics2018

Research online: Robust tightly coupled GNSS/INS estimation for navigation

Jan Skaloud, Omar Garcia Crespillo, Michael Meurer

We designed a tightly-coupled integration between GNSS and inertial navigation systems (INS) where we modify the update step of a classical Extended Kalman Filter (EKF) to consider different robust estimators (such as M-estimators). We consider different f ...

2017

Robust Tightly Coupled GNSS/INS Estimation for Navigation in Challenging Scenarios

Jan Skaloud, Omar Garcia Crespillo, Michael Meurer

The combination of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS) has become the baseline of many transportation applications. In this work, we design a tightly-coupled integration between GNSS and INS where we modify the u ...

2017

Robust compressive sensing of sparse signals: A review

Rafael Eduardo Carrillo Rangel, Alonso Ramirez Manzanares

Compressive sensing generally relies on the L2-norm for data fidelity, whereas in many applications robust estimators are needed. Among the scenarios in which robust performance is required, applications where the sampling process is performed in the prese ...

Springer International Publishing Ag2016

Measuring the effect of nuisance variables on classifiers

Pascal Frossard, Alhussein Fawzi

In real-world classification problems, nuisance variables can cause wild variability in the data. Nuisance corresponds for example to geometric distortions of the image, occlusions, illumination changes or any other deformations that do not alter the groun ...

2016

On the sensitivity of buildings to climate : the interaction of weather and building envelopes in determining future building energy consumption

Parag Rastogi

Building simulation requires a large number of uncertain inputs and parameters. These include quantities that may be known with reasonable confidence, like the thermal properties of materials and building dimensions, but also inputs whose correct values ca ...

EPFL2016

An overview of robust compressive sensing of sparse signals in impulsive noise

Rafael Eduardo Carrillo Rangel, Alonso Ramirez Manzanares

While compressive sensing (CS) has traditionally relied on L2 as an error norm, a broad spectrum of applications has emerged where robust estimators are required. Among those, applications where the sampling process is performed in the presence of impulsiv ...

2015

Function Computation over Networks : Efficient Information Processing for Cache and Sensor Applications

Chien-Yi Wang

This thesis looks at efficient information processing for two network applications: content delivery with caching and collecting summary statistics in wireless sensor networks. Both applications are studied under the same paradigm: function computation ove ...

EPFL2015