9 citations · 17 across the 5 of their papers we have counts for
9 papers · 1 filter
General bounds on the quality of Bayesian coresets
Trevor Campbell
Bayesian coresets speed up posterior inference in the large-scale data regime by approximating the full-data log-likelihood function with a surrogate log-likelihood based on a smal…
Embracing the chaos: analysis and diagnosis of numerical instability in variational flows
Zuheng Xu, Trevor Campbell
In this paper, we investigate the impact of numerical instability on the reliability of sampling, density evaluation, and evidence lower bound (ELBO) estimation in variational flow…
Slice Sampling for General Completely Random Measures
Peiyuan Zhu, Alexandre Bouchard-Côté, Trevor Campbell
Completely random measures provide a principled approach to creating flexible unsupervised models, where the number of latent features is infinite and the number of features that i…
Validated Variational Inference via Practical Posterior Error Bounds
Jonathan H. Huggins, Mikołaj Kasprzak, Trevor Campbell +1
Variational inference has become an increasingly attractive fast alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, a major obstacle to th…
Sparse Variational Inference: Bayesian Coresets from Scratch
Trevor Campbell, Boyan Beronov
The proliferation of automated inference algorithms in Bayesian statistics has provided practitioners newfound access to fast, reproducible data analysis and powerful statistical m…
Universal Boosting Variational Inference
Trevor Campbell, Xinglong Li
Boosting variational inference (BVI) approximates an intractable probability density by iteratively building up a mixture of simple component distributions one at a time, using tec…