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Redescending M-estimators

Related publications (31)

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We introduce Neural Network (NN for short) approximation architectures for the numerical solution of Boundary Integral Equations (BIEs for short). We exemplify the proposed NN approach for the boundary reduction of the potential problem in two spatial dime ...

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Diffusion Magnetic Resonance Imaging (dMRI) is a powerful non-invasive method for studying white matter tracts of the brain. However, accurate microstructure estimation with fiber orientation distribution (FOD) using existing computational methods requires ...

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Detecting whether a stochastic process is finitely expressed in a basis

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Is it possible to detect if the sample paths of a stochastic process almost surely admit a finite expansion with respect to some/any basis? The determination is to be made on the basis of a finite collection of discretely/noisily observed sample paths. We ...

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Density estimation in RKHS with application to Korobov spaces in high dimensions

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A kernel method for estimating a probability density function from an independent and identically distributed sample drawn from such density is presented. Our estimator is a linear combination of kernel functions, the coefficients of which are determined b ...

2023

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

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Closed form approximation methods for portfolio valuation and risk management

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Data-driven modeling of beam loss in the LHC

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In the Large Hadron Collider, the beam losses are continuously measured for machine protection. By design, most of the particle losses occur in the collimation system, where the particles with high oscilla-tion amplitudes or large momentum error are scrape ...

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Inspired by Sibson’s alpha-mutual information, we introduce a new parametric class of universal predictors. This class interpolates two well-known predictors, the mixture estimator, that includes the Laplace and the Krichevsky-Trofimov predictors, and the ...

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Distribution-on-distribution regression via optimal transport maps

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We present a framework for performing regression when both covariate and response are probability distributions on a compact interval. Our regression model is based on the theory of optimal transportation, and links the conditional Frechet mean of the resp ...

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Assessing the traversability of rugged terrain is a difficult challenge for legged robots, especially when they implement multiple, distinct gaits. We tackle this problem on the k-rock2 amphibious, sprawling gait robot by training a gait-dependent traversa ...

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Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

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We study the problem of learning unknown parameters of stochastic dynamical models from data. Often, these models are high dimensional and contain several scales and complex structures. One is then interested in obtaining a reduced, coarse-grained descript ...

EPFL2022