35 citations · 153 across the 16 of their papers we have counts for
4 papers · 1 filter
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}^…
Bayesian Modeling with Gaussian Processes using the GPstuff Toolbox
Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen +3
Gaussian processes (GP) are powerful tools for probabilistic modeling purposes. They can be used to define prior distributions over latent functions in hierarchical Bayesian models…
Speeding up the binary Gaussian process classification
Jarno Vanhatalo, Aki Vehtari
Gaussian processes (GP) are attractive building blocks for many probabilistic models. Their drawbacks, however, are the rapidly increasing inference time and memory requirement alo…
Gaussian Process Regression with a Student-t Likelihood
Pasi Jylänki, Jarno Vanhatalo, Aki Vehtari
This paper considers the robust and efficient implementation of Gaussian process regression with a Student-t observation model. The challenge with the Student-t model is the analyt…