4 papers
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…
Compact Policies for Fully-Observable Non-Deterministic Planning as SAT
Tomas Geffner, Hector Geffner
Fully observable non-deterministic (FOND) planning is becoming increasingly important as an approach for computing proper policies in probabilistic planning, extended temporal plan…