56 citations · 58 across the 4 of their papers we have counts for
4 papers
GPflowOpt: A Bayesian Optimization Library using TensorFlow
Nicolas Knudde, Joachim van der Herten, Tom Dhaene +1
A novel Python framework for Bayesian optimization known as GPflowOpt is introduced. The package is based on the popular GPflow library for Gaussian processes, leveraging the benef…
Hypervolume-based Multi-objective Bayesian Optimization with Student-t Processes
Joachim van der Herten, Ivo Couckuyt, Tom Dhaene
Student- processes have recently been proposed as an appealing alternative non-parameteric function prior. They feature enhanced flexibility and predictive variance. In this wor…
Active Learning for Approximation of Expensive Functions with Normal Distributed Output Uncertainty
Joachim van der Herten, Ivo Couckuyt, Dirk Deschrijver +1
When approximating a black-box function, sampling with active learning focussing on regions with non-linear responses tends to improve accuracy. We present the FLOLA-Voronoi method…
Fast Calculation of the Knowledge Gradient for Optimization of Deterministic Engineering Simulations
Joachim van der Herten, Ivo Couckuyt, Dirk Deschrijver +1
A novel efficient method for computing the Knowledge-Gradient policy for Continuous Parameters (KGCP) for deterministic optimization is derived. The differences with Expected Impro…