Publications associées à Averaging Stochastic Gradient Descent on Riemannian Manifolds

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Moment bounds and asymptotics for the stochastic wave equation

Robert Dalang, Le Chen

We consider the stochastic wave equation on the real line driven by space time white noise and with irregular initial data. We give bounds on higher moments and, for the hyperbolic Anderson model, explicit formulas for second moments. These bounds imply we ...

Elsevier Science Bv2015

Asynchronous Adaptation and Learning Over Networks—Part III: Comparison Analysis

Ali H. Sayed

In Part II of this paper, also in this issue, we carried out a detailed mean-square-error analysis of the performance of asynchronous adaptation and learning over networks under a fairly general model for asynchronous events including random topologies, ra ...

Institute of Electrical and Electronics Engineers2015

Space-time modelling of extreme events

Anthony Christopher Davison, Raphaël Huser

Max-stable processes are the natural analogues of the generalized extreme-value distribution when modelling extreme events in space and time. Under suitable conditions, these processes are asymptotically justified models for maxima of independent replicati ...

Wiley-Blackwell2014

Method to determine the distribution of temperature sensors

Martin Vetterli, David Atienza Alonso, Juri Ranieri, Amina Chebira, Alessandro Vincenzi

Apparatus comprising M sensors for measuring the temperature on M locations of the apparatus and an estimator configured to estimate a temperature vector of the apparatus with N temperature variables, whereby the estimator is configured to approximate the ...

2014

Adaptive Image Resizing Based on Continuous-Domain Stochastic Modeling

Michaël Unser, John Paul Ward, Aurélien Bourquard, Hagay Kirshner

We introduce an adaptive continuous-domain modeling approach to texture and natural images. The continuous-domain image is assumed to be a smooth function, and we embed it in a parameterized Sobolev space. We point out a link between Sobolev spaces and sto ...

Ieee-Inst Electrical Electronics Engineers Inc2014

A Euclidean Likelihood Estimator for Bivariate Tail Dependence

Michal Warchol, Boris Oumow

The spectral measure plays a key role in the statistical modeling of multivariate extremes. Estimation of the spectral measure is a complex issue, given the need to obey a certain moment condition. We propose a Euclidean likelihood-based estimator for the ...

Taylor & Francis Inc2013

Tangent space estimation for smooth embeddings of Riemannian manifolds

Pascal Frossard, Elif Vural, Hemant Tyagi

Numerous dimensionality reduction problems in data analysis involve the recovery of low-dimensional models or the learning of manifolds underlying sets of data. Many manifold learning methods require the estimation of the tangent space of the manifold at a ...

2013

Lorentzian Iterative Hard Thresholding: Robust Compressed Sensing With Prior Information

Rafael Eduardo Carrillo Rangel

Commonly employed reconstruction algorithms in compressed sensing (CS) use the L-2 norm as the metric for the residual error. However, it is well-known that least squares (LS) based estimators are highly sensitive to outliers present in the measurement vec ...

Institute of Electrical and Electronics Engineers2013

Averaging Stochastic Gradient Descent on Riemannian Manifolds

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Moment bounds and asymptotics for the stochastic wave equation

Asynchronous Adaptation and Learning Over Networks—Part III: Comparison Analysis

Space-time modelling of extreme events

Method to determine the distribution of temperature sensors

Time–Data Tradeoffs by Aggressive Smoothing

Adaptive Image Resizing Based on Continuous-Domain Stochastic Modeling

A Euclidean Likelihood Estimator for Bivariate Tail Dependence

Statistical Inference for Partially Observed Stochastic Epidemics

Tangent space estimation for smooth embeddings of Riemannian manifolds

Lorentzian Iterative Hard Thresholding: Robust Compressed Sensing With Prior Information

Adaptive Image Resizing Based on Continuous-Domain Stochastic Modeling

Space-time modelling of extreme events

Lorentzian Iterative Hard Thresholding: Robust Compressed Sensing With Prior Information

Tangent space estimation for smooth embeddings of Riemannian manifolds

Moment bounds and asymptotics for the stochastic wave equation

Statistical Inference for Partially Observed Stochastic Epidemics

A Euclidean Likelihood Estimator for Bivariate Tail Dependence

Time–Data Tradeoffs by Aggressive Smoothing

Asynchronous Adaptation and Learning Over Networks—Part III: Comparison Analysis

Method to determine the distribution of temperature sensors