295 citations · 1.1k across the 17 of their papers we have counts for
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Many Objective Bayesian Optimization
Lucia Asencio Martín, Eduardo C. Garrido-Merchán
Some real problems require the evaluation of expensive and noisy objective functions. Moreover, the analytical expression of these objective functions may be unknown. These functio…
Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints
Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
Real-world problems often involve the optimization of several objectives under multiple constraints. An example is the hyper-parameter tuning problem of machine learning algorithms…
Multi-class Gaussian Process Classification with Noisy Inputs
Carlos Villacampa-Calvo, Bryan Zaldivar, Eduardo C. Garrido-Merchán +1
It is a common practice in the machine learning community to assume that the observed data are noise-free in the input attributes. Nevertheless, scenarios with input noise are comm…
Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian Processes
Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
Bayesian Optimization (BO) methods are useful for optimizing functions that are expen- sive to evaluate, lack an analytical expression and whose evaluations can be contaminated by…
Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processes
Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
Bayesian optimization (BO) methods are useful for optimizing functions that are expensive to evaluate, lack an analytical expression and whose evaluations can be contaminated by no…