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ORCIDIngmar Schuster
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2020 – today
- 2021
[j4]Ingmar Schuster
, Ilja Klebanov:
Markov Chain Importance Sampling - A Highly Efficient Estimator for MCMC. J. Comput. Graph. Stat. 30(2): 260-268 (2021)- [j3]Patrick Gelß, Stefan Klus, Ingmar Schuster, Christof Schütte:
Feature space approximation for kernel-based supervised learning. Knowl. Based Syst. 221: 106935 (2021) - [c4]Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster, Urs M. Bergmann, Roland Vollgraf:
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows. ICLR 2021 - [c3]Kashif Rasul, Calvin Seward, Ingmar Schuster, Roland Vollgraf:
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting. ICML 2021: 8857-8868 - [i10]Kashif Rasul, Calvin Seward, Ingmar Schuster, Roland Vollgraf:
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting. CoRR abs/2101.12072 (2021) - 2020
- [j2]Stefan Klus, Ingmar Schuster, Krikamol Muandet:
Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces. J. Nonlinear Sci. 30(1): 283-315 (2020) - [j1]Ilja Klebanov, Ingmar Schuster, Timothy John Sullivan:
A Rigorous Theory of Conditional Mean Embeddings. SIAM J. Math. Data Sci. 2(3): 583-606 (2020) - [c2]Ingmar Schuster, Mattes Mollenhauer, Stefan Klus, Krikamol Muandet:
Kernel Conditional Density Operators. AISTATS 2020: 993-1004 - [i9]Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster, Urs Bergmann, Roland Vollgraf:
Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows. CoRR abs/2002.06103 (2020) - [i8]Patrick Gelß, Stefan Klus, Ingmar Schuster, Christof Schütte:
Feature space approximation for kernel-based supervised learning. CoRR abs/2011.12651 (2020)
2010 – 2019
- 2019
- [i7]Ingmar Schuster, Mattes Mollenhauer, Stefan Klus, Krikamol Muandet:
Kernel Conditional Density Operators. CoRR abs/1905.11255 (2019) - [i6]Kashif Rasul, Ingmar Schuster, Roland Vollgraf, Urs Bergmann:
Set Flow: A Permutation Invariant Normalizing Flow. CoRR abs/1909.02775 (2019) - 2018
- [i5]Ingmar Schuster, Ilja Klebanov:
Markov Chain Importance Sampling - a highly efficient estimator for MCMC. CoRR abs/1805.07179 (2018) - [i4]Stefan Klus, Sebastian Peitz, Ingmar Schuster:
Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions. CoRR abs/1805.10118 (2018) - [i3]Stefan Klus, Andreas Bittracher, Ingmar Schuster, Christof Schütte:
A kernel-based approach to molecular conformation analysis. CoRR abs/1809.11092 (2018) - 2017
- [c1]Ingmar Schuster, Heiko Strathmann, Brooks Paige, Dino Sejdinovic:
Kernel Sequential Monte Carlo. ECML/PKDD (1) 2017: 390-409 - [i2]Stefan Klus, Ingmar Schuster, Krikamol Muandet:
Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces. CoRR abs/1712.01572 (2017) - 2016
- [b1]Ingmar Schuster:
Probabilistic models of natural language semantics. Leipzig University, Germany, 2016 - 2014
- [i1]Ingmar Schuster, Patrick Jähnichen:
Bayesian factorization of joint categorical distributions for relational data and classical conditioning models. CoRR abs/1402.4279 (2014)
Coauthor Index
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