17 citations · 33 across the 10 of their papers we have counts for
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stat.ML2019★ 2 cited
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes
Creighton Heaukulani, Mark van der Wilk
We implement gradient-based variational inference routines for Wishart and inverse Wishart processes, which we apply as Bayesian models for the dynamic, heteroskedastic covariance…
stat.ML2019★ 2 cited
Variational inference for neural network matrix factorization and its application to stochastic blockmodeling
Onno Kampman, Creighton Heaukulani
We consider the probabilistic analogue to neural network matrix factorization (Dziugaite & Roy, 2015), which we construct with Bayesian neural networks and fit with variational inf…