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Found 11 papers, 2 papers with code
FunQuant: A R package to perform quantization in the context of rare events and time-consuming simulations
Quantization summarizes continuous distributions by calculating a discrete approximation.
A comparison of mixed-variables Bayesian optimization approaches
In this article, costly mixed problems are approached through Gaussian processes where the discrete variables are relaxed into continuous latent variables.
Revisiting Bayesian Optimization in the light of the COCO benchmark
It is commonly believed that Bayesian optimization (BO) algorithms are highly efficient for optimizing numerically costly functions.
A sampling criterion for constrained Bayesian optimization with uncertainties
The main contribution of this work is an acquisition criterion that accounts for both the average improvement in objective function and the constraint reliability.
TREGO: a Trust-Region Framework for Efficient Global Optimization
By following a classical scheme for the trust region (based on a sufficient decrease condition), the proposed algorithm enjoys global convergence properties, while departing from EGO only for a subset of optimization steps.
Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version
Parametric shape optimization aims at minimizing an objective function f(x) where x are CAD parameters.
Global sensitivity analysis for optimization with variable selection
However it does not account for the specific structure of optimization problems where we would like to identify which variables most lead to constraints satisfaction and low values of the objective function.
Targeting Solutions in Bayesian Multi-Objective Optimization: Sequential and Batch Versions
Multi-objective optimization aims at finding trade-off solutions to conflicting objectives.
Budgeted Multi-Objective Optimization with a Focus on the Central Part of the Pareto Front -- Extended Version
When the number of experiments is severely restricted and/or when the number of objectives increases, uncovering the whole set of Pareto optimal solutions is out of reach, even for surrogate-based approaches: the proposed solutions are sub-optimal or do not cover the front well.
Small ensembles of kriging models for optimization
The Efficient Global Optimization (EGO) algorithm uses a conditional Gaus-sian Process (GP) to approximate an objective function known at a finite number of observation points and sequentially adds new points which maximize the Expected Improvement criterion according to the GP.
An analytic comparison of regularization methods for Gaussian Processes
A measure for data-model discrepancy is proposed which serves for choosing a regularization technique. In the second part of the paper, a distribution-wise GP is introduced that interpolates Gaussian distributions instead of data points.
