6 citations · 11 across the 6 of their papers we have counts for
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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…
Analytic natural gradient updates for Cholesky factor in Gaussian variational approximation
Linda S. L. Tan
Natural gradients can improve convergence in stochastic variational inference significantly but inverting the Fisher information matrix is daunting in high dimensions. Moreover, in…
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…
Gaussian variational approximation with sparse precision matrices
Linda S. L. Tan, David J. Nott
We consider the problem of learning a Gaussian variational approximation to the posterior distribution for a high-dimensional parameter, where we impose sparsity in the precision m…