74 citations · 74 across the 1 of their papers we have counts for
5 papers
The Mondrian Kernel
Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani +2
We introduce the Mondrian kernel, a fast random feature approximation to the Laplace kernel. It is suitable for both batch and online learning, and admits a fast kernel-width-selec…
Measuring the reliability of MCMC inference with bidirectional Monte Carlo
Roger B. Grosse, Siddharth Ancha, Daniel M. Roy
Markov chain Monte Carlo (MCMC) is one of the main workhorses of probabilistic inference, but it is notoriously hard to measure the quality of approximate posterior samples. This c…
Training generative neural networks via Maximum Mean Discrepancy optimization
Gintare Karolina Dziugaite, Daniel M. Roy, Zoubin Ghahramani
We consider training a deep neural network to generate samples from an unknown distribution given i.i.d. data. We frame learning as an optimization minimizing a two-sample test sta…
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…
Bayesian Agglomerative Clustering with Coalescents
Yee Whye Teh, Hal Daumé, Daniel Roy
We introduce a new Bayesian model for hierarchical clustering based on a prior over trees called Kingman's coalescent. We develop novel greedy and sequential Monte Carlo inferences…