Invariant estimator

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Publications associées (30)

Multi-ion-sensing emulator and multivariate calibration optimization by machine learning models

Giovanni De Micheli, Sandro Carrara, Mandresy Ivan Ny Hanitra, Francesca Criscuolo

One paramount challenge in multi-ion-sensing arises from ion interference that degrades the accuracy of sensor calibration. Machine learning models are here proposed to optimize such multivariate calibration. However, the acquisition of big experimental da ...

2021

Virtual metrology as an approach for product quality estimation in Industry 4.0: a systematic review and integrative conceptual framework

Dimitrios Kyritsis, Gökan May, Foivos Psarommatis Giannakopoulos

Virtual metrology (VM) involves estimating a product's quality directly from production process data without physically measuring it. This enables the product quality of each unit of production to be monitored in real time, while preserving the process eff ...

TAYLOR & FRANCIS LTD2022

Bundle-Specific Axon Diameter Index as a New Contrast to Differentiate White Matter Tracts

Jean-Philippe Thiran, David Paul Roger Romascano, Gabriel Girard, Alessandro Daducci, Cristina Granziera, Muhamed Barakovic, Maxime Descoteaux, Giorgio Innocenti

In the central nervous system of primates, several pathways are characterized by different spectra of axon diameters. In vivo methods, based on diffusion-weighted magnetic resonance imaging, can provide axon diameter index estimates non-invasively. However ...

2021

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

Adaptive isogeometric analysis on two-dimensional trimmed domains based on a hierarchical approach

Annalisa Buffa, Pablo Antolin Sanchez, Rafael Vazquez Hernandez, Luca Coradello

The focus of this work is on the development of an error-driven isogeometric framework, capable of automatically performing an adaptive simulation in the context of second- and fourth-order, elliptic partial differential equations defined on two-dimensiona ...

2020

Some first results on the consistency of spatial regression with partial differential equation regularization

Fabio Nobile, Eleonora Arnone

We study the consistency of the estimator in spatial regression with partial differential equa-tion (PDE) regularization. This new smoothing technique allows to accurately estimate spatial fields over complex two-dimensional domains, starting from noisy ob ...

2022

Frequency domain theory for functional time series: Variance decomposition and an invariance principle

Neda Mohammadi Jouzdani

This paper is concerned with frequency domain theory for functional time series, which are temporally dependent sequences of functions in a Hilbert space. We consider a variance decomposition, which is more suitable for such a data structure than the varia ...

INT STATISTICAL INST2020

Frequency stability assessment of modern power systems: Models definition and parameters identification

Mario Paolone, Yihui Zuo, Giacomo Piero Schiapparelli

One of the fundamental concerns in the operation of modern power systems is the assessment of their frequency stability in case of inertia-reduction induced by the large share of power electronic interfaced resources. Within this context, the paper propose ...

2020

A hierarchical approach to the a posteriori error estimation of isogeometric Kirchhoff plates and Kirchhoff–Love shells

Annalisa Buffa, Pablo Antolin Sanchez, Luca Coradello

This work focuses on the development of a posteriori error estimates for fourth-order, elliptic, partial differential equations. In particular, we propose a novel algorithm to steer an adaptive simulation in the context of Kirchhoff plates and Kirchhoff–Lo ...

2020

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

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