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Concept

Cross-covariance matrix

Publications associées (20)

Probing the nature of Dark Energy through the study of large scale structures using spectroscopic surveys and their simulations

Andrei Variu

Measurements of large-scale structure (LSS), as performed on the largest 3D map of over two million extragalactic sources from the Sloan Digital Sky Survey, together with measurements of the cosmic microwave background (CMB) anisotropies, are in complete a ...

EPFL2023

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

FANOK: Knockoffs in Linear Time

Quentin Rebjock

We describe a series of algorithms that efficiently implement Gaussian model-X knockoffs to control the false discovery rate on large-scale feature selection problems. Identifying the knockoff distribution requires solving a large-scale semidefinite progra ...

SIAM PUBLICATIONS2021

Euclid : Effects of sample covariance on the number counts of galaxy clusters

Yi Wang, Georges Meylan

Aims. We investigate the contribution of shot-noise and sample variance to uncertainties in the cosmological parameter constraints inferred from cluster number counts, in the context of the Euclid survey. ...

EDP SCIENCES S A2021

Testing general relativity on cosmological scales at redshift z similar to 1.5 with quasar and CMB lensing

Cheng Zhao

We test general relativity (GR) at the effective redshift (z) over tilde similar to 1.5 by estimating the statistic E-G, a probe of gravity, on cosmological scales 19 - 190 h(-1)Mpc. This is the highest redshift and largest scale estimation of E-G so far. ...

OXFORD UNIV PRESS2021

Functional Data Analysis By Matrix Completion1

Victor Panaretos, Marie-Hélène Descary

Functional data analyses typically proceed by smoothing, followed by functional PCA. This paradigm implicitly assumes that rough variation is due to nuisance noise. Nevertheless, relevant functional features such as time-localised or short scale fluctuatio ...

INST MATHEMATICAL STATISTICS2019

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient

Mohammad Emtiyaz Khan

Uncertainty estimation in large deep-learning models is a computationally challenging task, where it is difficult to form even a Gaussian approximation to the posterior distribution. In such situations, existing methods usually resort to a diagonal approxi ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Chebyshev Inequalities for Products of Random Variables

Daniel Kuhn, Wolfram Wiesemann, Napat Rujeerapaiboon

We derive sharp probability bounds on the tails of a product of symmetric non-negative random variables using only information about their first two moments. If the covariance matrix of the random variables is known exactly, these bounds can be computed nu ...

2018

Functional data analysis by matrix completion

Marie-Hélène Descary

Traditional approaches to analysing functional data typically follow a two-step procedure, consisting in first smoothing and then carrying out a functional principal component analysis. The idea underlying this procedure is that functional data are well ap ...

EPFL2017

Multi-Period Portfolio Optimization: Translation of Autocorrelation Risk to Excess Variance

Napat Rujeerapaiboon

Growth-optimal portfolios are guaranteed to accumulate higher wealth than any other investment strategy in the long run. However, they tend to be risky in the short term. For serially uncorrelated markets, similar portfolios with more robust guarantees hav ...

2016

Optimal acoustic rake receiver

Martin Vetterli, Ivan Dokmanic, Robin Scheibler

An acoustic processing method for M acoustic receivers comprising the steps of: Determining a beamforming weight vector with M weights for the M acoustic receivers based on at least one the steering vector of at least one real acoustic source, on steering ...

2016

Dimensionality Reduction in Dynamic Optimization under Uncertainty

Napat Rujeerapaiboon

Dynamic optimization problems affected by uncertainty are ubiquitous in many application domains. Decision makers typically model the uncertainty through random variables governed by a probability distribution. If the distribution is precisely known, then ...

EPFL2016

Manifold Sparse Beamforming

Volkan Cevher, Afsaneh Asaei, Baran Gözcü

We consider the minimum variance distortionless response (MVDR) beamforming problems where the array covariance matrix is rank deficient. The conventional approach handles such rank-deficiencies via diagonal loading on the covariance matrix. In this settin ...

2013

Investigating Reliability on Fuel Cell Model Identification. Part III: Behavior of Assessment Criteria and Limits in Identification

Jan Van Herle, Daniel Favrat, Leonidas Tsikonis

The present paper is the third and last part of an investigation on what determines reliability in fuel cell model identification. In continuation to the effect of experimental design (Part I) and a process method for stochastic calculation of a model's pa ...

Wiley-V C H Verlag Gmbh2013

Multiple signal classification technique for phase estimation from a fringe pattern

Pramod Rastogi, Rajshekhar Gannavarpu

The paper introduces a multiple signal classification technique based method for fringe analysis. In the proposed method, the phase of a fringe pattern is locally approximated as a polynomial. The polynomial phase signal is then transformed to obtain signa ...

Optical Society of America2012

Investigating Reliability on Fuel Cell Model Identification. Part I: A Design of Experiments Approach

Jan Van Herle, Daniel Favrat, Leonidas Tsikonis

A model-based Design of Experiments method was employed for the optimisation of measurements on a Solid Oxide Fuel Cell (SOFC). Based on a simplified SOFC model, a variation of the D-optimality was used as optimisation criterion for the calculation of opti ...

2011

Spatio-Chromatic Decorrelation by Shift-Invariant Filtering

Pascal Fua, Sabine Süsstrunk, Matthew Brown

In this paper we derive convolutional filters for colour image whitening and decorrelation. Whilst whitening can be achieved via eigendecomposition of the image patch covariance, this operation is neither efficient nor biologically plausible. Given the shi ...

2011

Exponential Natural Evolution Strategies

Daniël Pieter Wierstra

The family of natural evolution strategies (NES) offers a principled approach to real-valued evolutionary optimization by following the natural gradient of the expected fitness. Like the well-known CMA-ES, the most competitive algorithm in the field, NES c ...

2010

Activity Level of an Atrial Ectopic Focus Observed Through the Equivalent Dipole: a Biophysical Model

Jean-Marc Vesin, Vincent Jacquemet, Mathieu Lemay, Cédric Duchene

Quantifying the role of atrial ectopic foci in the initiation and the maintenance of atrial fibrillation (AF) is a relevant clinical problem. This study aims at assessing the relation between the activity level of an ectopic focus and the complexity of the ...

2009

On the Long-Run Behavior of Equation-Based Rate Control

Jean-Yves Le Boudec, Milan Vojnovic

We consider unicast equation-based rate control, where, at some points in time, a sender adjusts its rate to f(p,r), where p is an on-line estimate of the loss-event rate observed by this source, r of the average round-trip time, and f is a TCP throughput ...

2005