Publications associées à The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

EPFL2024

Quantization for Decentralized Learning Under Subspace Constraints

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In this article, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained fo ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

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Control systems operating in real-world environments often face disturbances arising from measurement noise and model mismatch. These factors can significantly impact the perfor- mance and safety of the system. In this thesis, we aim to leverage data to de ...

EPFL2023

Regularization for distributionally robust state estimation and prediction

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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 ...

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Optimal digital filter selection for remote photoplethysmography (rPPG) signal conditioning

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Remote photoplethysmography (rPPG) using camera-based imaging has shown excellent potential recently in vital signs monitoring due to its contactless nature. However, the optimum filter selection for pre-processing rPPG data in signal conditioning is still ...

IOP Publishing Ltd2023

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

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The goal of this thesis is to study continuous-domain inverse problems for the reconstruction of sparse signals and to develop efficient algorithms to solve such problems computationally. The task is to recover a signal of interest as a continuous function ...

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An Efficient Signal-to-noise Approximation for Eccentric Inspiraling Binaries

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Eccentricity has emerged as a potentially useful tool for helping to identify the origin of black hole mergers. However, eccentric templates can be computationally very expensive owing to the large number of harmonics, making statistical analyses to distin ...

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Finite Bit Quantization For Decentralized Learning Under Subspace Constraints

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In this paper, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained form ...

IEEE2022

On Preconditioning of Decentralized Gradient-Descent When Solving a System of Linear Equations

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This article considers solving an overdetermined system of linear equations in peer-to-peer multiagent networks. The network is assumed to be synchronous and strongly connected. Each agent has a set of local data points, and their goal is to compute a line ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

PoGaIN: Poisson-Gaussian Image Noise Modeling From Paired Samples

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Image noise can often be accurately fitted to a Poisson-Gaussian distribution. However, estimating the distribution parameters from a noisy image only is a challenging task. Here, we study the case when paired noisy and noise-free samples are accessible. N ...

IEEE Institute of Electrical and Electronics Engineers2022

Spike-Based Sensing and Communication for Highly Energy-Efficient Sensor Edge Nodes

Mihai Adrian Ionescu, Teodor Rosca

Highly energy-efficient wireless sensor nodes are a prerequisite for a sustainable operation of the Internet of things. Therefore, classical approaches for system design based on digital signal processing are not a viable solution, but system design has to ...

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How Does Pre-Trained Wav2Vec 2.0 Perform On Domain-Shifted Asr? An Extensive Benchmark On Air Traffic Control Communications

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Recent work on self-supervised pre-training focus on leveraging large-scale unlabeled speech data to build robust end-to-end (E2E) acoustic models (AM) that can be later fine-tuned on downstream tasks e.g., automatic speech recognition (ASR). Yet, few work ...

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An All-Digital 1.25 Mbps 5-Subcarrier OFDM Backscatter Uplink with Delta-Sigma Modulation for Improved Spurious-Free Dynamic Range

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This paper describes an all-digital backscatter modulation approach leveraging delta-sigma modulation (DSM) to improve the in-channel spectral characteristics of orthogonal frequency division multiplexed (OFDM) backscatter communication. We demonstrate thr ...

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Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

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We study the problem of learning unknown parameters of stochastic dynamical models from data. Often, these models are high dimensional and contain several scales and complex structures. One is then interested in obtaining a reduced, coarse-grained descript ...

EPFL2022

A Noise-Resistant Mixed-Discrete Particle Swarm Optimization Algorithm for the Automatic Design of Robotic Controllers

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The automatic design of well-performing robotic controllers is still an unsolved problem due to the inherently large parameter space and noisy, often hard-to-define performance metrics, especially when sequential tasks need to be accomplished. Distal contr ...

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Sparsely Observed Functional Time Series: Theory and Applications

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Functional time series is a temporally ordered sequence of not necessarily independent random curves. While the statistical analysis of such data has been traditionally carried out under the assumption of completely observed functional data, it may well ha ...

EPFL2021

Convex optimization in sums of Banach spaces

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Model order reduction based on functional rational approximants for parametric PDEs with meromorphic structure

Davide Pradovera

Many engineering fields rely on frequency-domain dynamical systems for the mathematical modeling of physical (electrical/mechanical/etc.) structures. With the growing need for more accurate and reliable results, the computational burden incurred by frequen ...

EPFL2021

Multiple Line Outage Detection in Power Systems by Sparse Recovery Using Transient Data

Li Ding, Feng Liu, Ping Hu

Fast and accurate transmission line outage detection can help the central control unit to respond rapidly to better maintain the security and reliability of power systems. It is especially critical in the situation of multiple line outages which is more li ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Statistical limits of high-dimensional inference problems

Clément Dominique Luneau

This thesis focuses on two kinds of statistical inference problems in signal processing and data science. The first problem is the estimation of a structured informative tensor from the observation of a noisy tensor in which it is buried. The structure com ...

EPFL2021