70 citations · 172 across the 6 of their papers we have counts for
6 papers
A ranking approach to global optimization
Cédric Malherbe, Nicolas Vayatis
We consider the problem of maximizing an unknown function over a compact and convex set using as few observations as possible. We observe that the optimization of the function esse…
Stochastic Process Bandits: Upper Confidence Bounds Algorithms via Generic Chaining
Emile Contal, Nicolas Vayatis
The paper considers the problem of global optimization in the setup of stochastic process bandits. We introduce an UCB algorithm which builds a cascade of discretization trees base…
Optimization for Gaussian Processes via Chaining
Emile Contal, Cédric Malherbe, Nicolas Vayatis
In this paper, we consider the problem of stochastic optimization under a bandit feedback model. We generalize the GP-UCB algorithm [Srinivas and al., 2012] to arbitrary kernels an…
Gaussian Process Optimization with Mutual Information
Emile Contal, Vianney Perchet, Nicolas Vayatis
In this paper, we analyze a generic algorithm scheme for sequential global optimization using Gaussian processes. The upper bounds we derive on the cumulative regret for this gener…
Can Small Islands Protect Nearby Coasts From Tsunamis? An Active Experimental Design Approach
Themistoklis S. Stefanakis, Emile Contal, Nicolas Vayatis +2
Small islands in the vicinity of the mainland are believed to offer protection from wind and waves and thus coastal communities have been developed in these areas. However, what ha…
Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration
Emile Contal, David Buffoni, Alexandre Robicquet +1
In this paper, we consider the challenge of maximizing an unknown function f for which evaluations are noisy and are acquired with high cost. An iterative procedure uses the previo…