18 citations · 19 across the 2 of their papers we have counts for
5 papers
Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization
Lukas P. Fröhlich, Edgar D. Klenske, Julia Vinogradska +2
We consider the problem of robust optimization within the well-established Bayesian optimization (BO) framework. While BO is intrinsically robust to noisy evaluations of the object…
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems
Hans Kersting, Nicholas Krämer, Martin Schiegg +3
Likelihood-free (a.k.a. simulation-based) inference problems are inverse problems with expensive, or intractable, forward models. ODE inverse problems are commonly treated as likel…
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…
Trajectory-Based Off-Policy Deep Reinforcement Learning
Andreas Doerr, Michael Volpp, Marc Toussaint +2
Policy gradient methods are powerful reinforcement learning algorithms and have been demonstrated to solve many complex tasks. However, these methods are also data-inefficient, aff…
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…