133 citations · 499 across the 17 of their papers we have counts for
7 papers · 1 filter
Stochastic gradient variational Bayes for gamma approximating distributions
David A. Knowles
While stochastic variational inference is relatively well known for scaling inference in Bayesian probabilistic models, related methods also offer ways to circumnavigate the approx…
An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process
Amar Shah, David A. Knowles, Zoubin Ghahramani
Stochastic variational inference (SVI) is emerging as the most promising candidate for scaling inference in Bayesian probabilistic models to large datasets. However, the performanc…
Beta diffusion trees and hierarchical feature allocations
Creighton Heaukulani, David A. Knowles, Zoubin Ghahramani
We define the beta diffusion tree, a random tree structure with a set of leaves that defines a collection of overlapping subsets of objects, known as a feature allocation. A genera…
A reversible infinite HMM using normalised random measures
Konstantina Palla, David A. Knowles, Zoubin Ghahramani
We present a nonparametric prior over reversible Markov chains. We use completely random measures, specifically gamma processes, to construct a countably infinite graph with weight…
A dependent partition-valued process for multitask clustering and time evolving network modelling
Konstantina Palla, David A. Knowles, Zoubin Ghahramani
The fundamental aim of clustering algorithms is to partition data points. We consider tasks where the discovered partition is allowed to vary with some covariate such as space or t…
Gaussian Process Regression Networks
Andrew Gordon Wilson, David A. Knowles, Zoubin Ghahramani
We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric f…