no code implementations • ICLR 2022 • Lukas P. Fröhlich, Maksym Lefarov, Melanie N. Zeilinger, Felix Berkenkamp
In contrast, model-based methods can use the learned model to generate new data, but model errors and bias can render learning unstable or suboptimal.
1 code implementation • 7 Feb 2020 • Lukas P. Fröhlich, Edgar D. Klenske, Julia Vinogradska, Christian Daniel, Melanie N. Zeilinger
We consider the problem of robust optimization within the well-established Bayesian optimization (BO) framework.
no code implementations • 21 Jan 2020 • Lukas P. Fröhlich, Edgar D. Klenske, Christian G. Daniel, Melanie N. Zeilinger
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 and parameter optimization.
no code implementations • L4DC 2020 • Lukas Hewing, Elena Arcari, Lukas P. Fröhlich, Melanie N. Zeilinger
Second, we propose a linearization-based technique that directly provides approximations of the trajectory distribution, taking correlations explicitly into account.
2 code implementations • ICLR 2020 • Michael Volpp, Lukas P. Fröhlich, Kirsten Fischer, Andreas Doerr, Stefan Falkner, Frank Hutter, Christian Daniel
Transferring knowledge across tasks to improve data-efficiency is one of the open key challenges in the field of global black-box optimization.