113 citations · 320 across the 15 of their papers we have counts for
16 papers
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
Kashif Rasul, Calvin Seward, Ingmar Schuster +1
In this work, we propose \texttt{TimeGrad}, an autoregressive model for multivariate probabilistic time series forecasting which samples from the data distribution at each time ste…
Feature space approximation for kernel-based supervised learning
Patrick Gelß, Stefan Klus, Ingmar Schuster +1
We propose a method for the approximation of high- or even infinite-dimensional feature vectors, which play an important role in supervised learning. The goal is to reduce the size…
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster +2
Time series forecasting is often fundamental to scientific and engineering problems and enables decision making. With ever increasing data set sizes, a trivial solution to scale up…
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…