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Found 7 papers, 1 papers with code
Black-box constructions for exchangeable sequences of random multisets
We develop constructions for exchangeable sequences of point processes that are rendered conditionally-i. i. d.
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes
We implement gradient-based variational inference routines for Wishart and inverse Wishart processes, which we apply as Bayesian models for the dynamic, heteroskedastic covariance matrix of a multivariate time series.
Variational inference for neural network matrix factorization and its application to stochastic blockmodeling
We consider the probabilistic analogue to neural network matrix factorization (Dziugaite & Roy, 2015), which we construct with Bayesian neural networks and fit with variational inference.
Bayesian inference on random simple graphs with power law degree distributions
The BFRY random variables are well approximated by gamma random variables in a variational Bayesian inference routine, which we apply to several network datasets for which power law degree distributions are a natural assumption.
Gibbs-type Indian buffet processes
We investigate a class of feature allocation models that generalize the Indian buffet process and are parameterized by Gibbs-type random measures.
Beta diffusion trees and hierarchical feature allocations
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.
The combinatorial structure of beta negative binomial processes
sequences of Bernoulli processes with a common beta process base measure, in which case the combinatorial structure is described by the Indian buffet process.
