41 citations · 49 across the 5 of their papers we have counts for
6 papers
Active Learning of Continuous-time Bayesian Networks through Interventions
Dominik Linzner, Heinz Koeppl
We consider the problem of learning structures and parameters of Continuous-time Bayesian Networks (CTBNs) from time-course data under minimal experimental resources. In practice,…
Continuous-Time Bayesian Networks with Clocks
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. However, such models are ofte…
A Variational Perturbative Approach to Planning in Graph-based Markov Decision Processes
Dominik Linzner, Heinz Koeppl
Coordinating multiple interacting agents to achieve a common goal is a difficult task with huge applicability. This problem remains hard to solve, even when limiting interactions t…
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Dominik Linzner, Michael Schmidt, Heinz Koeppl
Continuous-time Bayesian Networks (CTBNs) represent a compact yet powerful framework for understanding multivariate time-series data. Given complete data, parameters and structure…
Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Dominik Linzner, Heinz Koeppl
Continuous-time Bayesian networks (CTBNs) constitute a general and powerful framework for modeling continuous-time stochastic processes on networks. This makes them particularly at…
Reservoir-induced Thouless pumping and symmetry protected topological order in open quantum chains
Dominik Linzner, Lukas Wawer, Fabian Grusdt +1
We introduce a classification scheme for symmetry protected topological phases applicable to stationary states of open systems based on a generalization of the many-body polarizati…