144 citations · 324 across the 35 of their papers we have counts for
5 papers · 1 filter
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…
Thompson Sampling with Approximate Inference
My Phan, Yasin Abbasi-Yadkori, Justin Domke
We study the effects of approximate inference on the performance of Thompson sampling in the -armed bandit problems. Thompson sampling is a successful algorithm for online decis…
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation
Justin Domke, Daniel Sheldon
Recent work in variational inference (VI) uses ideas from Monte Carlo estimation to tighten the lower bounds on the log-likelihood that are used as objectives. However, there is no…
Provable Gradient Variance Guarantees for Black-Box Variational Inference
Justin Domke
Recent variational inference methods use stochastic gradient estimators whose variance is not well understood. Theoretical guarantees for these estimators are important to understa…
Provable Smoothness Guarantees for Black-Box Variational Inference
Justin Domke
Black-box variational inference tries to approximate a complex target distribution though a gradient-based optimization of the parameters of a simpler distribution. Provable conver…