Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data. - Dominik Linzner; Heinz Koeppl (NeurIPS, 2018)

Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data. - Dominik Linzner; Michael Schmidt; Heinz Koeppl (NeurIPS, 2019)

A Variational Perturbative Approach to Planning in Graph-Based Markov Decision Processes. - Dominik Linzner; Heinz Koeppl (AAAI, 2020)

Continuous Time Bayesian Networks with Clocks. - Nicolai Engelmann; Dominik Linzner; Heinz Koeppl (ICML, 2020)

Active Learning of Continuous-time Bayesian Networks through Interventions. - Dominik Linzner; Heinz Koeppl (ICML, 2021)

Scalable Inference in Graph-coupled Continuous-time Markov Chains. - Dominik Linzner (2021)

BoFire: Bayesian Optimization Framework Intended for Real Experiments. - Johannes P. Dürholt; Thomas S. Asche; Johanna Kleinekorte; Gabriel Mancino-Ball; Benjamin Schiller; Simon Sung; Julian Keupp; Aaron Osburg; Toby Boyne; Ruth Misener; Rosona Eldred; Wagner Steuer Costa; et al. (CoRR, 2024)