21 citations · 23 across the 5 of their papers we have counts for
5 papers
Data-Efficient Interactive Multi-Objective Optimization Using ParEGO
Arash Heidari, Sebastian Rojas Gonzalez, Tom Dhaene +1
Multi-objective optimization is a widely studied problem in diverse fields, such as engineering and finance, that seeks to identify a set of non-dominated solutions that provide op…
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
Victor Picheny, Joel Berkeley, Henry B. Moss +13
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow. Our library enables th…
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…