Peer reviewed

K Rasul, AS Sheikh, I Schuster, U Bergmann, R Vollgraf. Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows. International Conference on Learning Representations (selected as spotlight presentation), 2021 (

I Schuster, I Klebanov. Markov Chain Importance Sampling – a highly efficient estimator for MCMC. Journal of Computational and Graphical Statistics, 2020 (

I Klebanov, I Schuster, T J Sullivan. A Rigorous Theory of Conditional Mean Embeddings. SIAM Journal on Mathematics of Data Science, 2020 (

I Schuster, M Mollenhauer, S Klus, K Muandet. Kernel Conditional Density Operators. AISTATS, 2020 (

M Mollenhauer, I Schuster, S Klus, C Schütte. Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces. Proceedings of the Workshop on Dynamics, Optimization and Computation, 2020 (

S Klus, I Schuster, K Muandet. Eigendecompositions of transfer operators in reproducing kernel Hilbert spaces. Journal of Nonlinear Science, 2020 (

S Klus, A Bittracher, I Schuster, C Schütte. A kernel-based approach to molecular conformation analysis. The Journal of chemical physics, 149(24), 2018 (

I Schuster, H Strathmann, B Paige and D Sejdinovic. Kernel Sequential Monte Carlo. ECML-PKDD, 2017 (

Technical reports and working papers

P Gelß, S Klus, I Schuster, C Schütte. Feature space approximation for kernel-based supervised learning, Working paper, 2020

I Schuster. Gradient Importance Sampling, Technical report, 2016 (

I Schuster. Consistency of Importance Sampling estimates based on dependent sample sets and an application to models with factorizing likelihoods, Technical report, 2015 (

I Schuster. A Bayesian Model of node interaction in networks, Technical report, 2014 (

I Schuster, T Krueger, C Gehl, K Rieck, and P Laskov. FIPS: FIRST intrusion prevention system. Technical Report 1, Fraunhofer FIRST, 2010.