Publication

Improved Inference On Risk Measures For Univariate Extremes

Publications associées (30)

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These datasets contain the total particle number concentrations and normalized size distributions (dN/dlogDp) of excited, fluorescent, and hyper-fluorescent particles of sizes 0.5 to 20 μm (optical diameter). The normalized size distribution datasets are s ...

Zenodo2023

Nonparametric estimation for SDE with sparsely sampled paths: An FDA perspective

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We consider the problem of nonparametric estimation of the drift and diffusion coefficients of a Stochastic Differential Equation (SDE), based on n independent replicates {Xi(t) : t is an element of [0 , 1]}13 d B(t), where alpha is an element of {0 , 1} a ...

Elsevier2024

Can one hear the shape of a target zone?

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2023

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 ...

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Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier f ...

Ieee Computer Soc2023

Multi-Level Monte Carlo Methods for Uncertainty Quantification and Risk-Averse Optimisation

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This work aims to study the effects of wind uncertainties in civil engineering structural design. Optimising the design of a structure for safety or operability without factoring in these uncertainties can result in a design that is not robust to these per ...

EPFL2022

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In this work, we consider the problem of estimating the probability distribution, the quantile or the conditional expectation above the quantile, the so called conditional-value-at-risk, of output quantities of complex random differential models by the MLM ...

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Universal inference enables the construction of confidence intervals and tests without regularity conditions by splitting the data into two parts and appealing to Markov's inequality. Previous investigations have shown that the cost of this generality is a ...

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Advances in computing have enabled widespread access to pose estimation, creating new sources of data streams. Unlike mock set-ups for data collection, tapping into these data streams through on-device active learning allows us to directly sample from the ...

BMVA Press2022

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