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Mirrored Langevin Dynamics

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Safe Adaptive Importance Sampling

Martin Jaggi, Sebastian Urban Stich, Anant Raj

Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants - using importance values defined by the complete gradient information which changes during optimization - ...

2017

Modelling across extremal dependence classes

Anthony Christopher Davison, Jonathan A. Tawn, Jennifer Lynne Wadsworth

Different dependence scenarios can arise in multivariate extremes, entailing careful selection of an appropriate class of models. In bivariate extremes, the variables are either asymptotically dependent or are asymptotically independent. Most available sta ...

Wiley-Blackwell2017

Shapes from Pixels

Mitra Fatemi

In today's digital world, sampling is at the heart of any signal acquisition device. Imaging devices are ubiquitous examples that capture two-dimensional visual signals and store them as the pixels of discrete images. The main concern is whether and how th ...

EPFL2016

Sampling Sparse Signals on the Sphere: Algorithms and Applications

Yue Lu, Ivan Dokmanic

We propose a sampling scheme that can perfectly reconstruct a collection of spikes on the sphere from samples of their lowpass-filtered observations. Central to our algorithm is a generalization of the annihilating filter method, a tool widely used in arra ...

Institute of Electrical and Electronics Engineers2016

Attributing Aliasing Artifacts in Dirty Intensity Fields to their Parent Source

Matthieu Martin Jean-André Simeoni

Estimating intensity fields of stochastic phenomenons is of crucial interest in many scientific applications. Typical experimental setups involve an acquisition system, that subsequently filters and samples the probed intensity field. This equivalently def ...

2016

Stochastic gradient descent with finite samples sizes

Ali H. Sayed, Stefan Vlaski, Bicheng Ying, Kun Yuan

The minimization of empirical risks over finite sample sizes is an important problem in large-scale machine learning. A variety of algorithms has been proposed in the literature to alleviate the computational burden per iteration at the expense of converge ...

IEEE2016

Detection over diffusion networks: Asymptotic tools for performance prediction and simulation

Ali H. Sayed

Exploiting recent progress [1]-[4] in the characterization of the detection performance of diffusion strategies over adaptive multi-agent networks: i) we present two theoretical approximations, one based on asymptotic normality and the other based on the t ...

IEEE2016

Random sampling of bandlimited signals on graphs

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We study the problem of sampling

k

-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, \ie, independent of the graph structure, and its perform ...

2015

Sampling-Based Nuclear Data Uncertainty Quantification for Continuous Energy Monte Carlo Codes

Ting Zhu

The goal of the present PhD research is to establish a methodology of nuclear data uncertainty quantification (NDUQ) for MCNPX, the continuous-energy Monte-Carlo (M-C) code. The high fidelity (continuous-energy treatment and flexible geometry modelling) of ...

EPFL2015

Discrete stochastic heat equation driven by fractional noise

Kamran Kalbasi

We consider the parabolic Anderson model on

\mathbb{Z}^d

driven by fractional noise. We prove that it has a mild solution given by Feynman-Kac representation which coincides with the partition function of a directed polymer in a fractional Brownian envir ...

EPFL2014