Publications associées à Regularization for distributionally robust state estimation and prediction

Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons

Sadegh Farhadkhani, Oscar Jean Olivier Villemaud, Julien René Pierre Fageot, Le Nguyen Hoang

Many applications, e.g. in content recommendation, sports, or recruitment, leverage the comparisons of alternatives to score those alternatives. The classical Bradley-Terry model and its variants have been widely used to do so. The historical model conside ...

AAAI Press2024

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

Data-Driven Unknown-Input Observers and State Estimation

Giancarlo Ferrari Trecate, Mustafa Sahin Turan

Unknown-input observers (UIOs) allow for estimation of the states of an LTI system without knowledge of all inputs. In this letter, we provide a novel data-driven UIO based on behavioral system theory and the result known as Fundamental Lemma proposed by J ...

2022

Aerosol formation and growth rates from chamber experiments using Kalman smoothing

Lubna Dada

Bayesian state estimation in the form of Kalman smoothing was applied to differential mobility analyser train (DMA-train) measurements of aerosol size distribution dynamics. Four experiments were analysed in order to estimate the aerosol size distribution, ...

Copernicus Publications2021

Adversarial Analytics

Viet Anh Nguyen

Adversarial learning is an emergent technique that provides better security to machine learning systems by deliberately protecting them against specific vulnerabilities of the learning algorithms. Many adversarial learning problems can be cast equivalently ...

EPFL2019

Distributed state estimation for discrete-time linear time invariant systems: A survey

Colin Neil Jones, Francisco Fernandes Castro Rego

Motivated by the increasing availability and quality of miniaturized sensors, computers, and wireless communication devices arid given their enormous potential, the use of wireless sensor networks (WSN) has become widespread. Because in many applications o ...

2019

Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler

Jean-Philippe Thiran, Gabriel Girard, Erick Jorge Canales Rodriguez, Jonathan Rafael Patino Lopez, Marco Pizzolato, Thomas Yu

Probabilistic parameter estimation in model fitting runs the gamut from maximum likelihood or maximum a posteriori point estimates from optimization to Markov Chain Monte Carlo (MCMC) sampling. The latter, while more computationally intensive, generally pr ...

SPRINGER INTERNATIONAL PUBLISHING AG2019

Distributed State Estimation and Cooperative Path-Following Under Communication Constraints

Francisco Fernandes Castro Rego

The main topics of this thesis are distributed estimation and cooperative path-following in the presence of communication constraints, with applications to autonomous marine vehicles. To this end, we study algorithms that take explicitly into account the c ...

EPFL2018

Blowing in the Wind : Regularizations and Outlier Removal

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

Design of a Distributed Quantized Luenberger Filter for Bounded Noise

Colin Neil Jones, Ye Pu, Andrea Alessandretti, Francisco Fernandes Castro Rego

This paper addresses the problem of distributed state estimation for linear systems with process and measurement noise, in the case of limited communication data rate, where the data exchanged between agents is quantized. We propose a linear distributed Lu ...

Ieee2016