33 citations · 229 across the 20 of their papers we have counts for
10 papers · 1 filter
Deep Classifiers with Label Noise Modeling and Distance Awareness
Vincent Fortuin, Mark Collier, Florian Wenzel +7
Uncertainty estimation in deep learning has recently emerged as a crucial area of interest to advance reliability and robustness in safety-critical applications. While there have b…
Bayesian Neural Network Priors Revisited
Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober +5
Isotropic Gaussian priors are the de facto standard for modern Bayesian neural network inference. However, it is unclear whether these priors accurately reflect our true beliefs ab…
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models
Théo Galy-Fajou, Florian Wenzel, Manfred Opper
We propose automated augmented conjugate inference, a new inference method for non-conjugate Gaussian processes (GP) models. Our method automatically constructs an auxiliary variab…
How Good is the Bayes Posterior in Deep Neural Networks Really?
Florian Wenzel, Kevin Roth, Bastiaan S. Veeling +7
During the past five years the Bayesian deep learning community has developed increasingly accurate and efficient approximate inference procedures that allow for Bayesian inference…
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
Théo Galy-Fajou, Florian Wenzel, Christian Donner +1
We propose a new scalable multi-class Gaussian process classification approach building on a novel modified softmax likelihood function. The new likelihood has two benefits: it lea…
Quasi-Monte Carlo Variational Inference
Alexander Buchholz, Florian Wenzel, Stephan Mandt
Many machine learning problems involve Monte Carlo gradient estimators. As a prominent example, we focus on Monte Carlo variational inference (MCVI) in this paper. The performance…
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…
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…