8 citations · 14 across the 3 of their papers we have counts for
8 papers
On Stein Variational Neural Network Ensembles
Francesco D'Angelo, Vincent Fortuin, Florian Wenzel
Ensembles of deep neural networks have achieved great success recently, but they do not offer a proper Bayesian justification. Moreover, while they allow for averaging of predictio…
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Florian Wenzel, Jasper Snoek, Dustin Tran +1
Ensembles over neural network weights trained from different random initialization, known as deep ensembles, achieve state-of-the-art accuracy and calibration. The recently introdu…
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…