897 citations · 2.2k across the 55 of their papers we have counts for
11 papers · 1 filter
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…
Scalable Generalized Dynamic Topic Models
Patrick Jähnichen, Florian Wenzel, Marius Kloft +1
Dynamic topic models (DTMs) model the evolution of prevalent themes in literature, online media, and other forms of text over time. DTMs assume that word co-occurrence statistics c…
Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation
Florian Wenzel, Theo Galy-Fajou, Christan Donner +2
We propose a scalable stochastic variational approach to GP classification building on Polya-Gamma data augmentation and inducing points. Unlike former approaches, we obtain closed…
Bayesian Nonlinear Support Vector Machines for Big Data
Florian Wenzel, Theo Galy-Fajou, Matthaeus Deutsch +1
We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the…
Distributed Optimization of Multi-Class SVMs
Maximilian Alber, Julian Zimmert, Urun Dogan +1
Training of one-vs.-rest SVMs can be parallelized over the number of classes in a straight forward way. Given enough computational resources, one-vs.-rest SVMs can thus be trained…
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…