no code implementations • 4 Apr 2021 • Joel Oren, Chana Ross, Maksym Lefarov, Felix Richter, Ayal Taitler, Zohar Feldman, Christian Daniel, Dotan Di Castro
This method can equally be applied to both the offline, as well as online, variants of the combinatorial problem, in which the problem components (e. g., jobs in scheduling problems) are not known in advance, but rather arrive during the decision-making process.
no code implementations • 1 Jan 2021 • Felix Berkenkamp, Anna Eivazi, Lukas Grossberger, Kathrin Skubch, Jonathan Spitz, Christian Daniel, Stefan Falkner
Transfer and meta-learning algorithms leverage evaluations on related tasks in order to significantly speed up learning or optimization on a new problem.
no code implementations • ICLR 2021 • Michael Volpp, Fabian Flürenbrock, Lukas Grossberger, Christian Daniel, Gerhard Neumann
Recently, casting probabilistic regression as a multi-task learning problem in terms of conditional latent variable (CLV) models such as the Neural Process (NP) has shown promising results.
no code implementations • ICML 2020 • Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig
To address this shortcoming, we employ Gaussian ODE filtering (a probabilistic numerical method for ODEs) to construct a local Gaussian approximation to the likelihood.
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.
2 code implementations • 14 May 2019 • Andreas Doerr, Michael Volpp, Marc Toussaint, Sebastian Trimpe, Christian Daniel
Policy gradient methods are powerful reinforcement learning algorithms and have been demonstrated to solve many complex tasks.
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.
2 code implementations • ICML 2018 • Andreas Doerr, Christian Daniel, Martin Schiegg, Duy Nguyen-Tuong, Stefan Schaal, Marc Toussaint, Sebastian Trimpe
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification.
no code implementations • NeurIPS 2013 • Alexandros Paraschos, Christian Daniel, Jan R. Peters, Gerhard Neumann
In order to use such a trajectory distribution for robot movement control, we analytically derive a stochastic feedback controller which reproduces the given trajectory distribution.