Publications related to Lorentzian Iterative Hard Thresholding: Robust Compressed Sensing With Prior Information

Asian option pricing with orthogonal polynomials

In this paper we derive a series expansion for the price of a continuously sampled arithmetic Asian option in the Black-Scholes setting. The expansion is based on polynomials that are orthogonal with respect to the log-normal distribution. All terms in the ...

ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD2019

Central limit theorems for multilevel Monte Carlo methods

Sebastian Krumscheid, Hakon Andreas Hoel

In this work, we show that uniform integrability is not a necessary condition for central limit theorems (CLT) to hold for normalized multilevel Monte Carlo (MLMC) estimators and we provide near optimal weaker conditions under which the CLT is achieved. In ...

2019

Comment: Models Are Approximations!

Anthony Christopher Davison, Erwan Fabrice Koch, Jonathan Koh Boon Han

This discussion focuses on areas of disagreement with the papers, particularly the target of inference and the case for using the robust 'sandwich' variance estimator in the presence of moderate mis-specification. We also suggest that existing procedures m ...

INST MATHEMATICAL STATISTICS2019

Averaging Stochastic Gradient Descent on Riemannian Manifolds

Nicolas Henri Bernard Flammarion

We propose an estimator for the mean of a random vector in Rd that can be computed in time O(n3.5 + n2d) for n i.i.d. samples and that has error bounds matching the sub-Gaussian case. The only assumptions we make about the data distribution are that it has ...

2018

Combining a 3D Reflex Based Neuromuscular Model with a State Estimator Based on Central Pattern Generators

Auke Ijspeert, Florin Dzeladini, Amy Roning Wu, Tycho Jan Henricus Brug

A neuromuscular model (NMC) presented by H. Geyer and extended by S. Song shows very interesting similarities with real human locomotion. The model uses a combination of reflex loops to generate stable locomotion and is able to cope with external disturban ...

Springer International Publishing Ag2017

Blowing in the Wind

Marta Martinez-Camara

Every day tons of pollutants are emitted into the atmosphere all around the world. These pollutants are altering the equilibrium of our planet, causing profound changes in its climate, increasing global temperatures, and raising the sea level. The need to ...

EPFL2017

Parameter Determination of Sensor Stochastic Models under Covariate Dependency

Jan Skaloud, Stéphane Guerrier, Philipp Clausen

The proliferation of (low-cost) sensors provokes new challenges in data fusion. This is related to the correctness of stochastic characterization that is a prerequisite for optimal estimation of parameters from redundant observations. Different (statistica ...

2017

Robust Distributed Estimation by Networked Agents

Ali H. Sayed, Ali Zoubir

Diffusion adaptive networks tasked with solving estimation problems have attracted attention in recent years due to their reliability, scalability, resource efficiency, and resilience to node and link failure. Diffusion adaptation strategies that are based ...

2017

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Martin Vetterli, Arash Amini, Mitra Fatemi

We present a sampling theory for a class of binary images with finite rate of innovation (FRI). Every image in our model is the restriction of

\mathds{1}_{\{p\leq0\}}

to the image plane, where

\mathds{1}

denotes the indicator function and

p

is some r ...

2016

Robust compressive sensing of sparse signals: A review

Alonso Ramirez Manzanares, Rafael Eduardo Carrillo Rangel

Compressive sensing generally relies on the L2-norm for data fidelity, whereas in many applications robust estimators are needed. Among the scenarios in which robust performance is required, applications where the sampling process is performed in the prese ...

Springer International Publishing Ag2016

Lorentzian Iterative Hard Thresholding: Robust Compressed Sensing With Prior Information

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Asian option pricing with orthogonal polynomials

Central limit theorems for multilevel Monte Carlo methods

Comment: Models Are Approximations!

Averaging Stochastic Gradient Descent on Riemannian Manifolds

Combining a 3D Reflex Based Neuromuscular Model with a State Estimator Based on Central Pattern Generators

Blowing in the Wind

Parameter Determination of Sensor Stochastic Models under Covariate Dependency

Robust Distributed Estimation by Networked Agents

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Robust compressive sensing of sparse signals: A review

Blowing in the Wind

Averaging Stochastic Gradient Descent on Riemannian Manifolds

Combining a 3D Reflex Based Neuromuscular Model with a State Estimator Based on Central Pattern Generators

Comment: Models Are Approximations!

Robust Distributed Estimation by Networked Agents

Asian option pricing with orthogonal polynomials

Robust compressive sensing of sparse signals: A review

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Central limit theorems for multilevel Monte Carlo methods

Parameter Determination of Sensor Stochastic Models under Covariate Dependency