144 citations · 324 across the 35 of their papers we have counts for
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Amortized Variational Inference for Simple Hierarchical Models
Abhinav Agrawal, Justin Domke
It is difficult to use subsampling with variational inference in hierarchical models since the number of local latent variables scales with the dataset. Thus, inference in hierarch…
Variational Marginal Particle Filters
Jinlin Lai, Justin Domke, Daniel Sheldon
Variational inference for state space models (SSMs) is known to be hard in general. Recent works focus on deriving variational objectives for SSMs from unbiased sequential Monte Ca…
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…
An Easy to Interpret Diagnostic for Approximate Inference: Symmetric Divergence Over Simulations
Justin Domke
It is important to estimate the errors of probabilistic inference algorithms. Existing diagnostics for Markov chain Monte Carlo methods assume inference is asymptotically exact, an…