128 citations · 293 across the 18 of their papers we have counts for
5 papers · 1 filter
Kernelised Normalising Flows
Eshant English, Matthias Kirchler, Christoph Lippert
Normalising Flows are non-parametric statistical models characterised by their dual capabilities of density estimation and generation. This duality requires an inherently invertibl…
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties
Jakob Lindinger, David Reeb, Christoph Lippert +1
Deep Gaussian Processes learn probabilistic data representations for supervised learning by cascading multiple Gaussian Processes. While this model family promises flexible predict…
Two-sample Testing Using Deep Learning
Matthias Kirchler, Shahryar Khorasani, Marius Kloft +1
We propose a two-sample testing procedure based on learned deep neural network representations. To this end, we define two test statistics that perform an asymptotic location test…
Integrating omics and MRI data with kernel-based tests and CNNs to identify rare genetic markers for Alzheimer's disease
Stefan Konigorski, Shahryar Khorasani, Christoph Lippert
For precision medicine and personalized treatment, we need to identify predictive markers of disease. We focus on Alzheimer's disease (AD), where magnetic resonance imaging scans p…
Sparse Probit Linear Mixed Model
Stephan Mandt, Florian Wenzel, Shinichi Nakajima +3
Linear Mixed Models (LMMs) are important tools in statistical genetics. When used for feature selection, they allow to find a sparse set of genetic traits that best predict a conti…