Ingmar Schuster
Ingmar Schuster
Senior Applied Scientist, Zalando SE
Verified email at - Homepage
Cited by
Cited by
Eigendecompositions of transfer operators in reproducing kernel Hilbert spaces
S Klus, I Schuster, K Muandet
Journal of Nonlinear Science 30 (1), 283-315, 2020
Gradient importance sampling
I Schuster
arXiv preprint arXiv:1507.05781, 2015
A kernel-based approach to molecular conformation analysis
S Klus, A Bittracher, I Schuster, C Schütte
The Journal of chemical physics 149 (24), 244109, 2018
Multivariate probabilistic time series forecasting via conditioned normalizing flows
K Rasul, AS Sheikh, I Schuster, U Bergmann, R Vollgraf
arXiv preprint arXiv:2002.06103, 2020
Singular value decomposition of operators on reproducing kernel Hilbert spaces
M Mollenhauer, I Schuster, S Klus, C Schütte
Proceedings of the Workshop on Dynamics, Optimization and Computation held …, 2020
Kernel conditional density operators
I Schuster, M Mollenhauer, S Klus, K Muandet
International Conference on Artificial Intelligence and Statistics, 993-1004, 2020
Markov Chain Importance Sampling—A Highly Efficient Estimator for MCMC
I Schuster, I Klebanov
Journal of Computational and Graphical Statistics, 1-9, 2020
A rigorous theory of conditional mean embeddings
I Klebanov, I Schuster, TJ Sullivan
SIAM Journal on Mathematics of Data Science 2 (3), 583-606, 2020
Kernel sequential monte carlo
I Schuster, H Strathmann, B Paige, D Sejdinovic
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017
Consistency of Importance Sampling estimates based on dependent sample sets and an application to models with factorizing likelihoods
I Schuster
arXiv preprint arXiv:1503.00357, 2015
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
K Rasul, C Seward, I Schuster, R Vollgraf
arXiv preprint arXiv:2101.12072, 2021
Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions
S Klus, S Peitz, I Schuster
arXiv preprint arXiv:1805.10118, 2018
Fips: First intrusion prevention system
I Schuster, T Krueger, C Gehl, K Rieck, P Laskov
Technical Report 1, Fraunhofer FIRST, 2010. URL http://publica. fraunhofer …, 0
Set Flow: A Permutation Invariant Normalizing Flow
K Rasul, I Schuster, R Vollgraf, U Bergmann
arXiv preprint arXiv:1909.02775, 2019
Exact active subspace Metropolis-Hastings, with applications to the Lorenz-96 system
I Schuster, PG Constantine, TJ Sullivan
arXiv preprint arXiv:1712.02749, 2017
Kernel techniques for adaptive Monte Carlo methods
H Strathmann, D Sejdinovic, S Livingston, I Schuster, M Lomeli Garcia, ...
Greek Stochastics Workshop on Big Data and Big Models, 2016
A Bayesian Model of node interaction in networks
I Schuster
arXiv preprint arXiv:1402.4279, 2014
Feature space approximation for kernel-based supervised learning
P Gelß, S Klus, I Schuster, C Schütte
Knowledge-Based Systems 221, 106935, 2021
Probabilistic models of natural language semantics
I Schuster
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