no code implementations • ICML 2020 • Nicolai Engelmann, Dominik Linzner, Heinz Koeppl
Structured stochastic processes evolving in continuous time present a widely adopted framework to model phenomena occurring in nature and engineering.
no code implementations • 31 May 2021 • Dominik Linzner, Heinz Koeppl
We propose a novel criterion for experimental design based on a variational approximation of the expected information gain.
no code implementations • 1 Jul 2020 • Nicolai Engelmann, Dominik Linzner, Heinz Koeppl
Structured stochastic processes evolving in continuous time present a widely adopted framework to model phenomena occurring in nature and engineering.
no code implementations • 4 Dec 2019 • Dominik Linzner, Heinz Koeppl
We present a novel approximate solution method for multi-agent Markov decision problems on graphs, based on variational perturbation theory.
no code implementations • NeurIPS 2019 • Dominik Linzner, Michael Schmidt, Heinz Koeppl
Instead of sampling and scoring all possible structures individually, we assume the generator of the CTBN to be composed as a mixture of generators stemming from different structures.
no code implementations • NeurIPS 2018 • Dominik Linzner, Heinz Koeppl
Existing approximation techniques, such as sampling and low-order variational methods, either scale unfavorably in system size, or are unsatisfactory in terms of accuracy.