5 citations · 6 across the 6 of their papers we have counts for
6 papers
Transfer Learning in Bayesian Optimization for Aircraft Design
Ali Tfaily, Youssef Diouane, Nathalie Bartoli +1
The use of transfer learning within Bayesian optimization addresses the disadvantages of the so-called \textit{cold start} problem by using source data to aid in the optimization o…
Risk averse constrained blackbox optimization under mixed aleatory/epistemic uncertainties
Charles Audet, Jean Bigeon, Romain Couderc +1
This paper addresses risk averse constrained optimization problems where the objective and constraint functions can only be computed by a blackbox subject to unknown uncertainties.…
Sequential stochastic blackbox optimization with zeroth-order gradient estimators
Charles Audet, Jean Bigeon, Romain Couderc +1
This work considers stochastic optimization problems in which the objective function values can only be computed by a blackbox corrupted by some random noise following an unknown d…
Parallel Surrogate-assisted Optimization Using Mesh Adaptive Direct Search
Bastien Talgorn, Stéphane Alarie, Michael Kokkolaras
We consider computationally expensive blackbox optimization problems and present a method that employs surrogate models and concurrent computing at the search step of the mesh adap…
Constrained stochastic blackbox optimization using a progressive barrier and probabilistic estimates
Kwassi Joseph Dzahini, Michael Kokkolaras, Sébastien Le Digabel
This work introduces the StoMADS-PB algorithm for constrained stochastic blackbox optimization, which is an extension of the mesh adaptive direct-search (MADS) method originally de…
StoMADS: Stochastic blackbox optimization using probabilistic estimates
Charles Audet, Kwassi Joseph Dzahini, Michael Kokkolaras +1
This work introduces StoMADS, a stochastic variant of the mesh adaptive direct-search (MADS) algorithm originally developed for deterministic blackbox optimization. StoMADS conside…