1 citations · 1 across the 2 of their papers we have counts for
5 papers
A Rigorous Theory of Conditional Mean Embeddings
Ilja Klebanov, Ingmar Schuster, T. J. Sullivan
Conditional mean embeddings (CMEs) have proven themselves to be a powerful tool in many machine learning applications. They allow the efficient conditioning of probability distribu…
Set Flow: A Permutation Invariant Normalizing Flow
Kashif Rasul, Ingmar Schuster, Roland Vollgraf +1
We present a generative model that is defined on finite sets of exchangeable, potentially high dimensional, data. As the architecture is an extension of RealNVPs, it inherits all i…
Kernel Conditional Density Operators
Ingmar Schuster, Mattes Mollenhauer, Stefan Klus +1
We introduce a novel conditional density estimation model termed the conditional density operator (CDO). It naturally captures multivariate, multimodal output densities and shows p…
Exact active subspace Metropolis-Hastings, with applications to the Lorenz-96 system
Ingmar Schuster, Paul G. Constantine, T. J. Sullivan
We consider the application of active subspaces to inform a Metropolis-Hastings algorithm, thereby aggressively reducing the computational dimension of the sampling problem. We sho…
Consistency of Importance Sampling estimates based on dependent sample sets and an application to models with factorizing likelihoods
Ingmar Schuster
In this paper, I proof that Importance Sampling estimates based on dependent sample sets are consistent under certain conditions. This can be used to reduce variance in Bayesian Mo…