We aim to optimize a black-box function f : X→ R under the assumption that f is H¨older smooth and has bounded norm in the Reproducing …

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We consider the problem of allocating samples to a finite set of discrete distributions in order to learn them uniformly well in terms …

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We construct and analyze active learning algorithms for the problem of binary classification with abstention. We consider three …

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We study the problem of efficient exploration in order to learn an accurate model of an environment, modeled as a Markov decision …

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In this paper, the problem of estimating the level set of a black-box function from noisy and expensive evaluation queries is …

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We consider the problem of training a machine learning model over a network of users in a fully decentralized framework. The users take …

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In this paper, the problem of maximizing a black-box function f:X→R is studied in the Bayesian framework with a Gaussian Process prior. …

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Species tree reconstruction from genomic data is increasingly performed using methods that account for sources of gene tree discordance …

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