35 citations · 153 across the 16 of their papers we have counts for
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stat.ME2017
Laplace approximation and the natural gradient for Gaussian process regression with the heteroscedastic Student-t model
Marcelo Hartmann, Jarno Vanhatalo
This paper considers the Laplace method to derive approximate inference for the Gaussian process (GP) regression in the location and scale parameters of the Student-t probabilistic…
stat.ML2017★ 3 cited
Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations
Eero Siivola, Aki Vehtari, Jarno Vanhatalo +2
Bayesian optimization (BO) is a global optimization strategy designed to find the minimum of an expensive black-box function, typically defined on a compact subset of $\mathcal{R}^…