Publications associées à Modelling across extremal dependence classes

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Near-Optimal Noisy Group Testing via Separate Decoding of Items

Volkan Cevher, Jonathan Mark Scarlett

In this paper, we revisit an efficient algorithm for noisy group testing in which each item is decoded separately (Malyutov and Mateev, 1980), and develop novel performance guarantees via an information-theoretic framework for general noise models. For the ...

IEEE2018

Mirrored Langevin Dynamics

Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Ali Kavis

We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design. We propose a unified framework, which is inspired by the classical mirror descent, to derive ...

2018

Mirrored Langevin Dynamics

Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Ali Kavis

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Near-Optimal Noisy Group Testing via Separate Decoding of Items

Volkan Cevher, Jonathan Mark Scarlett

The group testing problem consists of determining a small set of defective items from a larger set of items based on a number of tests, and is relevant in applications such as medical testing, communication protocols, pattern matching, and more. In this pa ...

2018

Bridging asymptotic independence and dependence in spatial extremes using Gaussian scale mixtures

Emeric Rolland Georges Thibaud, Raphaël Huser

Gaussian scale mixtures are constructed as Gaussian processes with a random variance. They have non-Gaussian marginals and can exhibit asymptotic dependence unlike Gaussian processes, which are asymptotically independent except in the case of perfect depen ...

Elsevier Sci Ltd2017

Robust Bounds In Multivariate Extremes

Sebastian Engelke

Extreme value theory provides an asymptotically justified framework for estimation of exceedance probabilities in regions where few or no observations are available. For multivariate tail estimation, the strength of extremal dependence is crucial and it is ...

Institute of Mathematical Statistics2017

A continuation multilevel Monte Carlo algorithm

Fabio Nobile, Erik Gustaf Bogislaw Von Schwerin

We propose a novel Continuation Multi Level Monte Carlo (CMLMC) algorithm for weak approximation of stochastic models. The CMLMC algorithm solves the given approximation problem for a sequence of decreasing tolerances, ending when the required error tolera ...

2015

MATHICSE Technical Report : A continuation multilevel Monte Carlo algorithm

Fabio Nobile, Erik Gustaf Bogislaw Von Schwerin

We propose a novel Continuation Multi Level Monte Carlo (CMLMC) algorithm for weak approximation of stochastic models that are described in terms of differential equations either driven by random measures or with random coefficients. The CMLMC algorithm so ...

MATHICSE2014

Modelling across extremal dependence classes

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Near-Optimal Noisy Group Testing via Separate Decoding of Items

Mirrored Langevin Dynamics

Mirrored Langevin Dynamics

Near-Optimal Noisy Group Testing via Separate Decoding of Items

Bridging asymptotic independence and dependence in spatial extremes using Gaussian scale mixtures

Robust Bounds In Multivariate Extremes

Generalized Pickands constants and stationary max-stable processes

A continuation multilevel Monte Carlo algorithm

MATHICSE Technical Report : A continuation multilevel Monte Carlo algorithm

Heavy-tail Phenomena: Spatio-temporal Extremal Dependence

Heavy-tail Phenomena: Spatio-temporal Extremal Dependence

Near-Optimal Noisy Group Testing via Separate Decoding of Items

Mirrored Langevin Dynamics

Near-Optimal Noisy Group Testing via Separate Decoding of Items

Robust Bounds In Multivariate Extremes

A continuation multilevel Monte Carlo algorithm

Mirrored Langevin Dynamics

Bridging asymptotic independence and dependence in spatial extremes using Gaussian scale mixtures

MATHICSE Technical Report : A continuation multilevel Monte Carlo algorithm

Generalized Pickands constants and stationary max-stable processes