16 citations · 31 across the 3 of their papers we have counts for
4 papers
Cautious Bayesian Optimization for Efficient and Scalable Policy Search
Lukas P. Fröhlich, Melanie N. Zeilinger, Edgar D. Klenske
Sample efficiency is one of the key factors when applying policy search to real-world problems. In recent years, Bayesian Optimization (BO) has become prominent in the field of rob…
Bayesian Optimization for Policy Search in High-Dimensional Systems via Automatic Domain Selection
Lukas P. Fröhlich, Edgar D. Klenske, Christian G. Daniel +1
Bayesian Optimization (BO) is an effective method for optimizing expensive-to-evaluate black-box functions with a wide range of applications for example in robotics, system design…
On Simulation and Trajectory Prediction with Gaussian Process Dynamics
Lukas Hewing, Elena Arcari, Lukas P. Fröhlich +1
Established techniques for simulation and prediction with Gaussian process (GP) dynamics often implicitly make use of an independence assumption on successive function evaluations…
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
Michael Volpp, Lukas P. Fröhlich, Kirsten Fischer +4
Transferring knowledge across tasks to improve data-efficiency is one of the open key challenges in the field of global black-box optimization. Readily available algorithms are typ…