Publications associées à Functional Data Analysis By Matrix Completion1

Transportation-based functional ANOVA and PCA for covariance operators

Victor Panaretos, Yoav Zemel, Valentina Masarotto

We consider the problem of comparing several samples of stochastic processes with respect to their second-order structure, and describing the main modes of variation in this second order structure, if present. These tasks can be seen as an Analysis of Vari ...

Inst Mathematical Statistics-Ims2024

TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression

Mathieu Salzmann, Alexandre Massoud Alahi, Megh Hiren Shukla

Deep heteroscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood. However, recent works show that this may result in sub-optimal convergence due to the challenges associated ...

2024

Stability: a search for structure

Wouter Jongeneel

In this thesis we study stability from several viewpoints. After covering the practical importance, the rich history and the ever-growing list of manifestations of stability, we study the following. (i) (Statistical identification of stable dynamical syste ...

EPFL2024

Random matrix methods for high-dimensional machine learning models

Antoine Philippe Michel Bodin

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

EPFL2024

Unlabeled Principal Component Analysis and Matrix Completion

Liangzu Peng, Yunzhen Yao

We introduce robust principal component analysis from a data matrix in which the entries of its columns have been corrupted by permutations, termed Unlabeled Principal Component Analysis (UPCA). Using algebraic geometry, we establish that UPCA is a well-de ...

Microtome Publishing2024

Robust tunable acoustic impedance control on electroacoustic resonators for aircraft noise reduction

Maxime Volery

Air traffic noise emissions are responsible for a significant part of the overall environmental noise, especially in the vicinity of airports. Exposure to environmental noise is known to negatively impact health and to be associated with cardiovascular dis ...

EPFL2023

Detecting whether a stochastic process is finitely expressed in a basis

Victor Panaretos, Neda Mohammadi Jouzdani

Is it possible to detect if the sample paths of a stochastic process almost surely admit a finite expansion with respect to some/any basis? The determination is to be made on the basis of a finite collection of discretely/noisily observed sample paths. We ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2023

The two-point correlation function covariance with fewer mocks

Cheng Zhao

We present FITCOV an approach for accurate estimation of the covariance of two-point correlation functions that requires fewer mocks than the standard mock-based covariance. This can be achieved by dividing a set of mocks into jackknife regions and fitting ...

Oxford Univ Press2023

Positive Definite Completions and Continuous Graphical Models

Kartik Waghmare

This thesis concerns the theory of positive-definite completions and its mutually beneficial connections to the statistics of function-valued or continuously-indexed random processes, better known as functional data analysis. In particular, it dwells upon ...

EPFL2023

Linear-Covariance Loss for End-to-End Learning of 6D Pose Estimation

Mathieu Salzmann, Yinlin Hu, Fulin Liu

Most modern image-based 6D object pose estimation methods learn to predict 2D-3D correspondences, from which the pose can be obtained using a PnP solver. Because of the non-differentiable nature of common PnP solvers, these methods are supervised via the i ...

Ieee Computer Soc2023

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Rita Drissi Daoudi

Laser Powder Bed Fusion (LPBF) is an Additive Manufacturing (AM) process consolidating parts layer by layer, from a metallic powder bed. It allows no limitation in terms of geometry and is therefore of particular interest to various industries. Metallic LP ...

EPFL2023

Improving algal bloom detection using spectroscopic analysis and machine learning: A case study in a large artificial reservoir, South Korea

Trung-Dung Hoang, Thanh Trung Huynh

The prediction of algal blooms using traditional water quality indicators is expensive, labor-intensive, and timeconsuming, making it challenging to meet the critical requirement of timely monitoring for prompt management. Using optical measures for foreca ...

ELSEVIER2023

Numerical study on the scale effect of tip vortex cavitation induced by incoming velocities and scale ratios

Mohamed Farhat, Huaiyu Cheng, Mohan Xu

Large eddy simulations of tip vortex cavitation (TVC) around an elliptical hydrofoil is performed to study its scale effect. A satisfying agreement is obtained between the numerical and experimental data. It indicates that the scale effect of TVC is remark ...

PERGAMON-ELSEVIER SCIENCE LTD2023

Functional data analysis with rough sample paths?

Victor Panaretos, Neda Mohammadi Jouzdani

Functional data are typically modeled as sample paths of smooth stochastic processes in order to mitigate the fact that they are often observed discretely and noisily, occasionally irregularly and sparsely. The smoothness assumption is imposed to allow for ...

TAYLOR & FRANCIS LTD2024

Community-based reconstruction and simulation of a full-scale model of region CA1 of rat hippocampus

Henry Markram, Felix Schürmann, James Gonzalo King, Srikanth Ramaswamy, Juan Bautista Hernando Vieites, Alexis Arnaudon, Eilif Benjamin Muller, Michael Reimann, Huanxiang Lu, Ying Shi, Marwan Muhammad Ahmed Abdellah, Vishal Sood, Jean-Denis Georges Emile Courcol, Werner Alfons Hilda Van Geit, Lida Kanari, Juan Luis Riquelme Roman, Michael Emiel Gevaert, Pramod Shivaji Kumbhar, Liesbeth Maria L Vanherpe, Armando Romani, Cyrille Pierre Henri Favreau, Michele Migliore, Mohameth François Sy, Szabolcs Kali, Judit Planas Carbonell, Genrich Ivaska, Sàra Sàray, Carmen Alina Lupascu, Rosanna Migliore, Audrey Mercer, András Ecker, Alberto Antonietti, Cristina Colangelo, Anna-Kristin Kaufmann, Julian Martin Budd, Elisabetta Giacalone, Thomas Brice Delemontex, Davide Bella, Fabien Petitjean, , , , ,

Validation of semi-analytical, semi-empirical covariance matrices for two-point correlation function for early DESI data

Cheng Zhao

We present an extended validation of semi-analytical, semi-empirical covariance matrices for the two-point correlation function (2PCF) on simulated catalogs representative of luminous red galaxies (LRGs) data collected during the initial 2 months of operat ...

OXFORD UNIV PRESS2023

Testing For The Rank Of A Covariance Operator

Victor Panaretos

How can we discern whether the covariance operator of a stochastic pro-cess is of reduced rank, and if so, what its precise rank is? And how can we do so at a given level of confidence? This question is central to a great deal of methods for functional dat ...

INST MATHEMATICAL STATISTICS-IMS2022

Random Surface Covariance Estimation by Shifted Partial Tracing

Victor Panaretos, Tomas Masák

The problem of covariance estimation for replicated surface-valued processes is examined from the functional data analysis perspective. Considerations of statistical and computational efficiency often compel the use of separability of the covariance, even ...

2023

Plasma-based Electroacoustic Actuator for Broadband Sound Absorption

Stanislav Sergeev

Environmental noise, mostly related to human activities, has an immense impact on public health. The development of noise reduction technologies is paramount in addressing this problem. Because of practical and economic reasons, a compact, broadband, light ...

EPFL2022

Covariance Estimation for Random Surfaces beyond Separability

Tomas Masák

This thesis focuses on non-parametric covariance estimation for random surfaces, i.e.~functional data on a two-dimensional domain. Non-parametric covariance estimation lies at the heart of functional data analysis, and considerations of statistical and com ...

EPFL2022