Gilles Stoltz

Publications15
h-index8
Citations635
Highly Influential Citations103
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Kullback–Leibler upper confidence bounds for optimal sequential allocation
We consider optimal sequential allocation in the context of the so-called stochastic multi-armed bandit model. We describe a generic index policy, in the sense of Gittins (1979), based on upper… Expand
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Improved second-order bounds for prediction with expert advice
TLDR
We derive new and sharper regret bounds for the well-known exponentially weighted average forecaster and for a second forecaster with a different multiplicative update rule. Expand
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Mirror Descent Meets Fixed Share (and feels no regret)
TLDR
A unified analysis of mirror descent, fixed share, and the generalized fixed share of [6] for online convex optimization in the simplex. Expand
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Minimizing regret with label efficient prediction
TLDR
We investigate label efficient prediction, a variant, proposed by Helmbold and Panizza, of the problem of prediction with expert advice. Expand
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Minimizing Regret with Label Efficient Prediction
TLDR
We investigate label efficient prediction, a variant of the problem of prediction with expert advice, proposed by Helmbold and Panizza, in which the forecaster does not have access to the outcomes of the sequence to be predicted unless he asks for it, which he can do for a limited number of times. Expand
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Ozone ensemble forecast with machine learning algorithms
TLDR
We apply machine learning algorithms to perform sequential aggregation of ozone forecasts on the basis of ensemble simulations and past observations. Expand
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Forecasting electricity consumption by aggregating specialized experts A review of the sequential aggregation of specialized experts, with an application to Slovakian and French country-wide…
We consider the setting of sequential prediction of arbitrary sequences based on specialized experts. We first provide a review of the relevant literature and present two theoretical contributions: a… Expand
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Aggregation of sleeping predictors to forecast electricity consumption
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Sequential model aggregation for production forecasting
TLDR
We propose to investigate the potential of machine learning algorithms to predict the future production of a reservoir based on past production data without model calibration. Expand
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Adaptation to the Range in $K$-Armed Bandits
We consider stochastic bandit problems with $K$ arms, each associated with a bounded distribution supported on the range $[m,M]$. We do not assume that the range $[m,M]$ is known and show that there… Expand