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Copulas are useful tools for the construction of multivariate models because they allow to link univariate marginals into a joint model with arbitrary dependence structure. While non-parametric copula models can have poor generalization performance, standard parametric copulas often lack expressive ...

Large multilayer neural networks trained with backpropagation have recently achieved state-of-the-art results in a wide range of problems. However, using backprop for neural net learning still has some disadvantages, e.g., having to tune a large number of hyperparameters to the data, lack of calibra ...

Unknown constraints arise in many types of expensive black-box optimization problems. Several methods have been proposed recently for performing Bayesian optimization with constraints, based on the expected improvement (EI) heuristic. However, EI can lead to pathologies when used with constraints. F ...