Publications related to Are Gaussian data all you need? the extents and limits of universality in high-dimensional generalized linear estimation

Related publications (9)

On Hagedorn wavepackets associated with different Gaussians

Hagedorn functions are carefully constructed generalizations of Hermite functions to the setting of many-dimensional squeezed and coupled harmonic systems. Wavepackets formed by superpositions of Hagedorn functions have been successfully used to solve the ...

IOP Publishing2025

Structural equation models for multivariate extremes

Mario Krali

This thesis contributes to the modelling of multivariate rare events by combining extreme value theory and graphical modelling. The central theme involves structural equation models (SEMs), which are powerful tools used to express causal extremal dependenc ...

EPFL2025

Predicting individual thermal preferences in an office: Assessing the performance of mixed-effects models

Matteo Favero

Multiple studies suggested that existing thermal comfort models inadequately predict occupants' individual thermal preferences. Personalised comfort models offer an alternative to conventional comfort models aiming to forecast individual's thermal preferen ...

PERGAMON-ELSEVIER SCIENCE LTD2024

Exact recovery and Bregman hard clustering of node-attributed Stochastic Block Model

Maximilien Claude Robert Dreveton

Network clustering tackles the problem of identifying sets of nodes (communities) that have similar connection patterns. However, in many scenarios, nodes also have attributes that are correlated with the clustering structure. Thus, network information (ed ...

2023

On Perfect Clustering of High Dimension, Low Sample Size Data

Soham Sarkar

Popular clustering algorithms based on usual distance functions (e.g., the Euclidean distance) often suffer in high dimension, low sample size (HDLSS) situations, where concentration of pairwise distances and violation of neighborhood structure have advers ...

IEEE COMPUTER SOC2020

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

Pascal Frossard, Arun Venkitaraman

In presence of sparse noise we propose kernel regression for predicting output vectors which are smooth over a given graph. Sparse noise models the training outputs being corrupted either with missing samples or large perturbations. The presence of sparse ...

IEEE2019

Jointly Informative Feature Selection Made Tractable by Gaussian Modeling

François Fleuret, Leonidas Lefakis

2016

A Novel Bayesian Impulse Radio Ultra-WideBand Ranging Algorithm

Jean-Yves Le Boudec

We consider the problem of ranging with Impulse Radio (IR) Ultra-WideBand (UWB) radio under weak Line Of Sight (LOS) environments and additive Gaussian noise. We use a Bayesian approach where the prior distribution of the channel follows the IEEE 802.15.4a ...

IEEE2009

Implicit meshes : unifying implicit and explicit surface representations for 3D reconstruction and tracking

Slobodan Ilic

This thesis proposes novel ways both to represent the static surfaces, and to parameterize their deformations. This can be used both by automated algorithms for efficient 3–D shape reconstruction, and by graphics designers for editing and animation. Deform ...

EPFL2005