Search Results for author: Christian Daniel

Found 9 papers, 4 papers with code

SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems

no code implementations4 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.

Combinatorial Optimization Decision Making +2

Probabilistic Meta-Learning for Bayesian Optimization

no code implementations1 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.

Meta-Learning Transfer Learning

Bayesian Context Aggregation for Neural Processes

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.

Bayesian Inference Multi-Task Learning +1

Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems

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.

Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization

1 code implementation7 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.

Trajectory-Based Off-Policy Deep Reinforcement Learning

2 code implementations14 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.

Continuous Control Policy Gradient Methods +3

Probabilistic Movement Primitives

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.

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