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20142023
most citedEfficient prediction of grain boundary energies from atomistic simulations via sequential design

1 citations · 3 across the 9 of their papers we have counts for

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Showing 2019Show all

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math.ST2019

Pointwise adaptive kernel density estimation under local approximate differential privacy

Martin Kroll

We consider non-parametric density estimation in the framework of local approximate differential privacy. In contrast to centralized privacy scenarios with a trusted curator, in th…

math.ST2019

Local differential privacy: Elbow effect in optimal density estimation and adaptation over Besov ellipsoids

Cristina Butucea, Amandine Dubois, Martin Kroll +1

We address the problem of non-parametric density estimation under the additional constraint that only privatised data are allowed to be published and available for inference. For t…