6 citations · 6 across the 5 of their papers we have counts for
6 papers
Generalized reparametrized variational Bayes with skew-symmetric normalization
Aoxiang Chen, Linda S. L. Tan
Bayesian hierarchical models with high-dimensional latent structure require scalable posterior approximations that preserve key dependencies while remaining computationally tractab…
Weighted Fisher divergence for high-dimensional Gaussian variational inference
Aoxiang Chen, David J. Nott, Linda S. L. Tan
Bayesian inference has many advantages for complex models, but standard Monte Carlo methods for summarizing the posterior can be computationally demanding, and it is attractive to…
Variational inference based on a subclass of closed skew normals
Linda S. L. Tan, Aoxiang Chen
Gaussian distributions are widely used in Bayesian variational inference to approximate intractable posterior densities, but the ability to accommodate skewness can improve approxi…
Second order stochastic gradient update for Cholesky factor in Gaussian variational approximation from Stein's Lemma
Linda S. L. Tan
In stochastic variational inference, use of the reparametrization trick for the multivariate Gaussian gives rise to efficient updates for the mean and Cholesky factor of the covari…
Conditionally structured variational Gaussian approximation with importance weights
Linda S. L. Tan, Aishwarya Bhaskaran, David J. Nott
We develop flexible methods of deriving variational inference for models with complex latent variable structure. By splitting the variables in these models into "global" parameters…
Bayesian variational inference for exponential random graph models
Linda S. L. Tan, Nial Friel
Deriving Bayesian inference for exponential random graph models (ERGMs) is a challenging "doubly intractable" problem as the normalizing constants of the likelihood and posterior d…