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Found 10 papers, 1 papers with code
Bayesian sequential design of computer experiments for quantile set inversion
We consider an unknown multivariate function representing a system-such as a complex numerical simulator-taking both deterministic and uncertain inputs.
Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method
This article focuses on the multi-objective optimization of stochastic simulators with high output variance, where the input space is finite and the objective functions are expensive to evaluate.
Relaxed Gaussian process interpolation: a goal-oriented approach to Bayesian optimization
This work presents a new procedure for obtaining predictive distributions in the context of Gaussian process (GP) modeling, with a relaxation of the interpolation constraints outside some ranges of interest: the mean of the predictive distributions no longer necessarily interpolates the observed values when they are outside ranges of interest, but are simply constrained to remain outside.
Parameter selection in Gaussian process interpolation: an empirical study of selection criteria
This article revisits the fundamental problem of parameter selection for Gaussian process interpolation.
Numerical issues in maximum likelihood parameter estimation for Gaussian process interpolation
This article investigates the origin of numerical issues in maximum likelihood parameter estimation for Gaussian process (GP) interpolation and investigates simple but effective strategies for improving commonly used open-source software implementations.
Sequential design of multi-fidelity computer experiments: maximizing the rate of stepwise uncertainty reduction
This article deals with the sequential design of experiments for (deterministic or stochastic) multi-fidelity numerical simulators, that is, simulators that offer control over the accuracy of simulation of the physical phenomenon or system under study.
Towards new cross-validation-based estimators for Gaussian process regression: efficient adjoint computation of gradients
We consider the problem of estimating the parameters of the covariance function of a Gaussian process by cross-validation.
Sequential design of experiments to estimate a probability of exceeding a threshold in a multi-fidelity stochastic simulator
We consider a Bayesian estimator of the probability, together with an associated measure of uncertainty, and propose a new multi-fidelity sequential design strategy, called Maximum Speed of Uncertainty Reduction (MSUR), to select the value of physical inputs and the fidelity level of new simulations.
A Bayesian approach to constrained single- and multi-objective optimization
More specifically, an extended domination rule is used to handle objectives and constraints in a unified way, and a corresponding expected hyper-volume improvement sampling criterion is proposed.
A new integral loss function for Bayesian optimization
In the special case of a one-step Bayes-optimal strategy, it leads to the classical Expected Improvement (EI) sampling criterion.
