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Wasserstein Distributionally Robust Learning

Publications associées (368)

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Statistical analysis of clusters of extreme events

Mária Süveges

The thesis is a contribution to extreme-value statistics, more precisely to the estimation of clustering characteristics of extreme values. One summary measure of the tendency to form groups is the inverse average cluster size. In extreme-value context, th ...

EPFL2009

Nonideal sampling and regularized interpolation of noisy data

Sathish Ramani

Conventional sampling (Shannon's sampling formulation and its approximation-theoretic counterparts) and interpolation theories provide effective solutions to the problem of reconstructing a signal from its samples, but they are primarily restricted to the ...

EPFL2009

Multi-experts scheduling system for real-time applications

Mario Bellino

This thesis focuses on the decisional process of autonomous systems, and more particularly, on the way to take a decision when the time at disposal in order to assess the whole situation is shorter than necessary. Indeed, numerous systems propose solutions ...

EPFL2009

Learning with Compressible Priors

Volkan Cevher

We describe a set of probability distributions, dubbed compressible priors, whose independent and identically distributed (iid) realizations result in p-compressible signals. A signal x in R^N is called p-compressible with magnitude R if its sorted coeffic ...

2009

A Novel Bayesian Impulse Radio Ultra-WideBand Ranging Algorithm

Jean-Yves Le Boudec, John Farserotu, Hai Zhan

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

One Step Impulse Radio Ultra-WideBand Positioning Algorithm

Jean-Yves Le Boudec, John Farserotu, Hai Zhan

We consider the problem of positioning estimation with impulse radio (IR) ultra-wideband (UWB) radio under dense multipaths and additive Gaussian noise environments. Most popular positioning algorithms first estimate certain parameters (such as time of arr ...

IEEE2009

Novel Algorithms on Impulse Radio Ultra-WideBand Ranging

Jean-Yves Le Boudec, John Farserotu, Hai Zhan

We consider the problem of ranging with impulse radio (IR) ultra-wideband (UWB) radio under dense multipaths propagation environments and additive Gaussian noise. We propose a Bayesian detection algorithm where the prior distribution of the channel follows ...

IEEE2009

Essays on individual decision making under uncertainty

Elise Le Nestour

This research project is an experimental study of decision-making in very difficult contexts resembling those encountered in financial markets. The starting point was the empirical observation that financial assets are objects of a very complex kind. Speci ...

EPFL2009

Predicting spacial similarity of freshwater fish biodiversity

Andrea Rinaldo, Mihaela Marian

A major issue in modern ecology is to understand how ecological complexity at broad scales is regulated by mechanisms operating at the organismic level. What specific underlying processes are essential for a macroecological pattern to emerge? Here, we anal ...

2009

Saddlepoint approximation for mixture models

Anthony Christopher Davison

Two-component mixture distributions with one component a point mass and the other a continuous density may be used as priors for Bayesian inference when sparse representation of an underlying signal is required. We show how saddlepoint approximation in suc ...

2009