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MCMC Variational Inference via Uncorrected Hamiltonian Annealing
Tomas Geffner, Justin Domke
Given an unnormalized target distribution we want to obtain approximate samples from it and a tight lower bound on its (log) normalization constant log Z. Annealed Importance Sampl…
Empirical Evaluation of Biased Methods for Alpha Divergence Minimization
Tomas Geffner, Justin Domke
In this paper we empirically evaluate biased methods for alpha-divergence minimization. In particular, we focus on how the bias affects the final solutions found, and how this depe…
Approximation Based Variance Reduction for Reparameterization Gradients
Tomas Geffner, Justin Domke
Flexible variational distributions improve variational inference but are harder to optimize. In this work we present a control variate that is applicable for any reparameterizable…
A Rule for Gradient Estimator Selection, with an Application to Variational Inference
Tomas Geffner, Justin Domke
Stochastic gradient descent (SGD) is the workhorse of modern machine learning. Sometimes, there are many different potential gradient estimators that can be used. When so, choosing…
Using Large Ensembles of Control Variates for Variational Inference
Tomas Geffner, Justin Domke
Variational inference is increasingly being addressed with stochastic optimization. In this setting, the gradient's variance plays a crucial role in the optimization procedure, sin…