Ingmar Schuster

Generating author description...

All published works

Action	Title	Year	Authors
+	Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows	2021	Kashif Rasul Abdul-Saboor Sheikh Ingmar Schuster Urs Bergmann Roland Vollgraf
+ PDF Chat	Feature space approximation for kernel-based supervised learning	2021	Patrick Gelß Stefan Klus Ingmar Schuster Christof Schütte
+	Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting	2021	Kashif Rasul Calvin Seward Ingmar Schuster Roland Vollgraf
+	Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting	2021	Kashif Rasul Calvin Seward Ingmar Schuster Roland Vollgraf
+ PDF Chat	Markov Chain Importance Sampling—A Highly Efficient Estimator for MCMC	2020	Ingmar Schuster Ilja Klebanov
+	Markov Chain Importance Sampling – a highly efficient estimator for MCMC	2020	Ingmar Schuster Ilja Klebanov
+ PDF Chat	Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces	2020	Mattes Mollenhauer Ingmar Schuster Stefan Klus Christof Schütte
+ PDF Chat	A Rigorous Theory of Conditional Mean Embeddings	2020	Ilja Klebanov Ingmar Schuster T. J. Sullivan
+	Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows	2020	Kashif Rasul Abdul-Saboor Sheikh Ingmar Schuster Urs Bergmann Roland Vollgraf
+ PDF Chat	Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces	2019	Stefan Klus Ingmar Schuster Krikamol Muandet
+	Kernel Conditional Density Operators	2019	Ingmar Schuster Mattes Mollenhauer Stefan Klus Krikamol Muandet
+	Set Flow: A Permutation Invariant Normalizing Flow	2019	Kashif Rasul Ingmar Schuster Roland Vollgraf Urs Bergmann
+	Kernel Conditional Density Operators	2019	Ingmar Schuster Mattes Mollenhauer Stefan Klus Krikamol Muandet
+ PDF Chat	A kernel-based approach to molecular conformation analysis	2018	Stefan Klus Andreas Bittracher Ingmar Schuster Christof Schütte
+	Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions.	2018	Stefan Klus Sebastian Peitz Ingmar Schuster
+	Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions	2018	Stefan Klus Sebastian Peitz Ingmar Schuster
+	Markov Chain Importance Sampling -- a highly efficient estimator for MCMC	2018	Ingmar Schuster Ilja Klebanov
+ PDF Chat	Kernel Sequential Monte Carlo	2017	Ingmar Schuster Heiko Strathmann Brooks Paige Dino Sejdinović
+	Exact active subspace Metropolis-Hastings, with applications to the Lorenz-96 system	2017	Ingmar Schuster Paul G. Constantine T. J. Sullivan
+	Kernel techniques for adaptive Monte Carlo methods	2016	Heiko Strathmann Dino Sejdinović Samuel A. Livingston Ingmar Schuster Maria Lomeli Garcia Zoltán Szabó Christophe Andrieu Arthur Gretton
+	Kernel Adaptive Sequential Monte Carlo	2015	Ingmar Schuster Heiko Strathmann Brooks Paige Dino Sejdinović
+	Kernel Sequential Monte Carlo	2015	Ingmar Schuster Heiko Strathmann Brooks Paige Dino Sejdinović
+	Gradient Importance Sampling	2015	Ingmar Schuster
+	Consistency of Importance Sampling estimates based on dependent sample sets and an application to models with factorizing likelihoods	2015	Ingmar Schuster
+	Kernel Sequential Monte Carlo	2015	Ingmar Schuster Heiko Strathmann Brooks Paige Dino Sejdinović
+	A Bayesian Model of node interaction in networks	2014	Ingmar Schuster
+	Bayesian factorization of joint categorical distributions for relational data and classical conditioning models.	2014	Ingmar Schuster Patrick Jähnichen
+	A Bayesian Model of node interaction in networks	2014	Ingmar Schuster

Common Coauthors

Coauthor	Papers Together
Stefan Klus	8
Kashif Rasul	5
Roland Vollgraf	5
Dino Sejdinović	5
Heiko Strathmann	5
Ilja Klebanov	4
Brooks Paige	4
Mattes Mollenhauer	3
Krikamol Muandet	3
Christof Schütte	3
Urs Bergmann	3
Abdul-Saboor Sheikh	2
T. J. Sullivan	2
Calvin Seward	2
Sebastian Peitz	2
Paul G. Constantine	1
Arthur Gretton	1
Maria Lomeli Garcia	1
Zoltán Szabó	1
Andreas Bittracher	1
Patrick Gelß	1
Christophe Andrieu	1
Samuel A. Livingston	1
Patrick Jähnichen	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	Joint measures and cross-covariance operators	1973	C. Richard Baker	6
+ PDF Chat	Kernel Mean Embedding of Distributions: A Review and Beyond	2017	Krikamol Muandet Kenji Fukumizu Bharath K. Sriperumbudur Bernhard Schölkopf	5
+	Adam: A Method for Stochastic Optimization	2014	Diederik P. Kingma Jimmy Ba	5
+ PDF Chat	Adaptive importance sampling in general mixture classes	2008	Olivier Cappé Randal Douc Arnaud Guillin Jean‐Michel Marin Christian P. Robert	5
+	Population Monte Carlo	2004	Olivier Cappé A Guillin Jean‐Michel Marin Christian P. Robert	5
+	Spatial Point Processes	2011	Mark Huber	4
+ PDF Chat	Monte Carlo Statistical Methods	2000	Hoon Kim Christian P. Robert George Casella	4
+ PDF Chat	Data-Driven Model Reduction and Transfer Operator Approximation	2018	Stefan Klus Feliks Nüske Péter Koltai Hao Wu Ioannis G. Kevrekidis Christof Schütte Frank Noé	4
+ PDF Chat	Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods	2011	Mark Girolami Ben Calderhead	4
+ PDF Chat	Dimensionality Reduction for Supervised Learning With Reproducing Kernel Hilbert Spaces	2003	Kenji Fukumizu Francis R. Bach Michael I. Jordan	4
+ PDF Chat	Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces	2019	Stefan Klus Ingmar Schuster Krikamol Muandet	4
+ PDF Chat	Sequential Monte Carlo Samplers	2006	Pierre Del Moral Arnaud Doucet Ajay Jasra	4
+	Kernel Bayes' rule: Bayesian inference with positive definite kernels	2013	Kenji Fukumizu Le Song Arthur Gretton	4
+ PDF Chat	Adaptive Multiple Importance Sampling	2012	Jean‐Marie Cornuet Jean‐Michel Marin Antonietta Mira Christian P. Robert	4
+	Kernel Adaptive Metropolis-Hastings	2014	Dino Sejdinović Heiko Strathmann Maria Lomeli Garcia Christophe Andrieu Arthur Gretton	4
+ PDF Chat	An Adaptive Metropolis Algorithm	2001	Heikki Haario Eero Saksman J. Tamminen	3
+	MCMC-Driven Adaptive Multiple Importance Sampling	2015	Luca Martino Vı́ctor Elvira David Luengo Jukka Corander	3
+	Sequential Monte Carlo on large binary sampling spaces	2011	Christian Schäfer Nicolás Chopin	3
+	Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling	2014	Jun‐Young Chung Çaǧlar Gülçehre Kyunghyun Cho Yoshua Bengio	3
+ PDF Chat	Wormhole Hamiltonian Monte Carlo	2014	Shiwei Lan Jeffrey Streets Babak Shahbaba	3
+ PDF Chat	A Family of Nonparametric Density Estimation Algorithms	2012	Esteban G. Tabak Cristina Turner	3
+	Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift	2015	Sergey Ioffe Christian Szegedy	3
+	On the numerical approximation of the Perron-Frobenius and Koopman operator	2016	Christof Schütte Péter Koltai Stefan Klus	3
+	A tutorial on adaptive MCMC	2008	Christophe Andrieu Johannes Thoms	3
+	N-BEATS: Neural basis expansion analysis for interpretable time series forecasting	2019	Boris N. Oreshkin Dmitri Carpov Nicolas Chapados Yoshua Bengio	3
+	DeepAR: Probabilistic forecasting with autoregressive recurrent networks	2019	David Salinas Valentín Flunkert Jan Gasthaus Tim Januschowski	3
+ PDF Chat	Consistency of adaptive importance sampling and recycling schemes	2019	Jean‐Michel Marin Pierre Pudlo Mohammed Sedki	3
+ PDF Chat	Kernel Mean Embedding of Distributions: A Review and Beyond	2017	Krikamol Muandet Kenji Fukumizu Bharath K. Sriperumbudur Bernhard Schölkopf	3
+ PDF Chat	Importance Sampling Squared for Bayesian Inference in Latent Variable Models	2014	Minh‐Ngoc Tran Marcel Scharth M. Pitt Robert Kohn	3
+	A note on the evaluation of generative models	2015	Lucas Theis Aäron van den Oord Matthias Bethge	3
+ PDF Chat	Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks	2019	Ekaterina Tolstaya Fernando Gama James Paulos George J. Pappas Vijay Kumar Alejandro Ribeiro	3
+ PDF Chat	MCMC Using Hamiltonian Dynamics	2011	Radford M. Neal	3
+ PDF Chat	Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks	2018	Guokun Lai Wei-Cheng Chang Yiming Yang Hanxiao Liu	3
+	GO‐GARCH: a multivariate generalized orthogonal GARCH model	2002	Roy van der Weide	3
+ PDF Chat	An Adaptive Sequential Monte Carlo Sampler	2013	Paul Fearnhead Benjamín M. Taylor	3
+	Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting	2019	Shiyang Li Xiaoyong Jin Yao Xuan Xiyou Zhou Wenhu Chen Yu-Xiang Wang Xifeng Yan	3
+	An Introduction to Sequential Monte Carlo Methods	2001	Arnaud Doucet Nando de Freitas Neil Gordon	3
+	Density estimation using Real NVP	2016	Laurent Dinh Jascha Sohl‐Dickstein Samy Bengio	3
+	Regularization of Inverse Problems	1996	Heinz W. Engl Martin Hanke Andreas B. Neubauer	3
+	Density Estimation in Infinite Dimensional Exponential Families	2013	Bharath K. Sriperumbudur Kenji Fukumizu Arthur Gretton Aapo Hyvärinen Revant Kumar	2
+ PDF Chat	Rates of convergence of the Hastings and Metropolis algorithms	1996	Kerrie Mengersen Richard L. Tweedie	2
+ PDF Chat	On a Generalization of the Preconditioned Crank–Nicolson Metropolis Algorithm	2016	Daniel Rudolf Björn Sprungk	2
+	Density estimation using Real NVP	2016	Laurent Dinh Jascha Sohl‐Dickstein Samy Bengio	2
+	Bayesian Model Choice Via Markov Chain Monte Carlo Methods	1995	Bradley P. Carlin Siddhartha Chib	2
+	Bayesian Learning via Stochastic Gradient Langevin Dynamics	2011	Max Welling Yee Whye Teh	2
+	MCMC Methods for Functions: Modifying Old Algorithms to Make Them Faster	2013	Simon L. Cotter Gareth O. Roberts Andrew M. Stuart David White	2
+ PDF Chat	Layered adaptive importance sampling	2016	Luca Martino Vı́ctor Elvira David Luengo Jukka Corander	2
+	Optimal scaling for various Metropolis-Hastings algorithms	2001	Gareth O. Roberts Jeffrey S. Rosenthal	2
+ PDF Chat	Theoretical Guarantees for Approximate Sampling from Smooth and Log-Concave Densities	2016	Arnak S. Dalalyan	2
+	Multiple Imputation for Nonresponse in Surveys	1987	Donald B. Rubin	2