924 citations · 5.3k across the 63 of their papers we have counts for
5 papers · 1 filter
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server
Leonard Hasenclever, Stefan Webb, Thibaut Lienart +4
This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stochastic natural gradient expectation propagation (SNEP), a novel alternative to e…
Approximate Inference with the Variational Holder Bound
Guillaume Bouchard, Balaji Lakshminarayanan
We introduce the Variational Holder (VH) bound as an alternative to Variational Bayes (VB) for approximate Bayesian inference. Unlike VB which typically involves maximization of a…
Mondrian Forests for Large-Scale Regression when Uncertainty Matters
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Many real-world regression problems demand a measure of the uncertainty associated with each prediction. Standard decision forests deliver efficient state-of-the-art predictive per…
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages
Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess +4
We propose an efficient nonparametric strategy for learning a message operator in expectation propagation (EP), which takes as input the set of incoming messages to a factor node,…
Particle Gibbs for Bayesian Additive Regression Trees
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Additive regression trees are flexible non-parametric models and popular off-the-shelf tools for real-world non-linear regression. In application domains, such as bioinformatics, w…