Expected improvement for expensive optimization: a review
The expected improvement (EI) algorithm is a very popular method for expensive
optimization problems. In the past twenty years, the EI criterion has been extended to deal …
optimization problems. In the past twenty years, the EI criterion has been extended to deal …
A supermartingale approach to Gaussian process based sequential design of experiments
Gaussian process (GP) models have become a well-established framework for the adaptive
design of costly experiments, and notably of computer experiments. GP-based sequential …
design of costly experiments, and notably of computer experiments. GP-based sequential …
Offline contextual bayesian optimization
In black-box optimization, an agent repeatedly chooses a configuration to test, so as to find
an optimal configuration. In many practical problems of interest, one would like to optimize …
an optimal configuration. In many practical problems of interest, one would like to optimize …
Continuous multi-task bayesian optimisation with correlation
This paper considers the problem of simultaneously identifying the optima for a (continuous
or discrete) set of correlated tasks, where the performance of a particular input parameter on …
or discrete) set of correlated tasks, where the performance of a particular input parameter on …
CO-BED: information-theoretic contextual optimization via Bayesian experimental design
We formalize the problem of contextual optimization through the lens of Bayesian
experimental design and propose CO-BED—a general, model-agnostic framework for …
experimental design and propose CO-BED—a general, model-agnostic framework for …
Bayesian optimization of expected quadratic loss for multiresponse computer experiments with internal noise
MHY Tan - SIAM/ASA Journal on Uncertainty Quantification, 2020 - SIAM
Design of systems based on computer simulations is prevalent. An important idea to improve
design quality, called robust parameter design (RPD), is to optimize control factors based on …
design quality, called robust parameter design (RPD), is to optimize control factors based on …
Learning to climb: Constrained contextual bayesian optimisation on a multi-modal legged robot
Controlling a legged robot to climb obstacles with different heights is challenging, but
important for an autonomous robot to work in an unstructured environment. In this paper, we …
important for an autonomous robot to work in an unstructured environment. In this paper, we …
Fast update of conditional simulation ensembles
Gaussian random field (GRF) conditional simulation is a key ingredient in many spatial
statistics problems for computing Monte-Carlo estimators and quantifying uncertainties on …
statistics problems for computing Monte-Carlo estimators and quantifying uncertainties on …
Uncertainty quantification on Pareto fronts and high-dimensional strategies in Bayesian optimization, with applications in multi-objective automotive design
M Binois - 2015 - theses.hal.science
This dissertation deals with optimizing expensive or time-consuming black-box functionsto
obtain the set of all optimal compromise solutions, ie the Pareto front. In automotivedesign …
obtain the set of all optimal compromise solutions, ie the Pareto front. In automotivedesign …
Practical bayesian optimization of objectives with conditioning variables
Bayesian optimization is a class of data efficient model based algorithms typically focused
on global optimization. We consider the more general case where a user is faced with …
on global optimization. We consider the more general case where a user is faced with …