317 citations · 721 across the 36 of their papers we have counts for
7 papers · 1 filter
Edge-exchangeable graphs and sparsity (NIPS 2016)
Diana Cai, Trevor Campbell, Tamara Broderick
Many popular network models rely on the assumption of (vertex) exchangeability, in which the distribution of the graph is invariant to relabelings of the vertices. However, the Ald…
Exchangeable Trait Allocations
Trevor Campbell, Diana Cai, Tamara Broderick
Trait allocations are a class of combinatorial structures in which data may belong to multiple groups and may have different levels of belonging in each group. Often the data are a…
Small-Variance Nonparametric Clustering on the Hypersphere
Julian Straub, Trevor Campbell, Jonathan P. How +1
Structural regularities in man-made environments reflect in the distribution of their surface normals. Describing these surface normal distributions is important in many computer v…
Coresets for Scalable Bayesian Logistic Regression
Jonathan H. Huggins, Trevor Campbell, Tamara Broderick
The use of Bayesian methods in large-scale data settings is attractive because of the rich hierarchical models, uncertainty quantification, and prior specification they provide. St…
Edge-exchangeable graphs and sparsity
Tamara Broderick, Diana Cai
A known failing of many popular random graph models is that the Aldous-Hoover Theorem guarantees these graphs are dense with probability one; that is, the number of edges grows qua…
Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures
Julian Straub, Trevor Campbell, Jonathan P. How +1
Point cloud alignment is a common problem in computer vision and robotics, with applications ranging from 3D object recognition to reconstruction. We propose a novel approach to th…
Truncated Random Measures
Trevor Campbell, Jonathan H. Huggins, Jonathan P. How +1
Completely random measures (CRMs) and their normalizations are a rich source of Bayesian nonparametric priors. Examples include the beta, gamma, and Dirichlet processes. In this pa…