research
∙
02/16/2023
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
We present Trieste, an open-source Python package for Bayesian optimizat...
share
research
∙
04/11/2022
{PF}^2ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization Under Unknown Constraints
We present Parallel Feasible Pareto Frontier Entropy Search ({PF}^2ES) –...
research
∙
01/26/2021
Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems
Given their substantial success in addressing a wide range of computer v...
research
∙
11/10/2017
GPflowOpt: A Bayesian Optimization Library using TensorFlow
A novel Python framework for Bayesian optimization known as GPflowOpt is...
research
∙
02/02/2017
Sensitivity Analysis of Expensive Black-Box Systems Using Metamodeling
Simulations are becoming ever more common as a tool for designing comple...
research
∙
08/18/2016
Active Learning for Approximation of Expensive Functions with Normal Distributed Output Uncertainty
When approximating a black-box function, sampling with active learning f...
research
∙
08/16/2016
