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Found 4 papers, 1 papers with code
Variational Inference with Numerical Derivatives: variance reduction through coupling
The Black Box Variational Inference (Ranganath et al. (2014)) algorithm provides a universal method for Variational Inference, but taking advantage of special properties of the approximation family or of the target can improve the convergence speed significantly.
A deterministic and computable Bernstein-von Mises theorem
Bernstein-von Mises results (BvM) establish that the Laplace approximation is asymptotically correct in the large-data limit.
Computing the quality of the Laplace approximation
In this work, we present a theorem upper-bounding the KL divergence between a log-concave target density $f\left(\boldsymbol{\theta}\right)$ and its Laplace approximation $g\left(\boldsymbol{\theta}\right)$.
Expectation Propagation performs a smoothed gradient descent
More precisely, we link it to using gradient descent to compute the Laplace approximation of a target probability distribution.
