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Rejection Sampling Variational Inference

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  • UserFrancisco J. R. Ruiz (Columbia University & University of Cambridge) World_link
  • ClockTuesday 22 November 2016, 11:30-12:30
  • HouseCBL Room BE-438.

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Talk based on https://arxiv.org/abs/1610.05683, for which the abstract is:

“Variational inference using the reparameterization trick has enabled large-scale approximate Bayesian inference in complex probabilistic models, leveraging stochastic optimization to sidestep intractable expectations. The reparameterization trick is applicable when we can simulate a random variable by applying a (differentiable) deterministic function on an auxiliary random variable whose distribution is fixed. For many distributions of interest (such as the gamma or Dirichlet), simulation of random variables relies on rejection sampling. The discontinuity introduced by the accept—reject step means that standard reparameterization tricks are not applicable. We propose a new method that lets us leverage reparameterization gradients even when variables are outputs of a rejection sampling algorithm. Our approach enables reparameterization on a larger class of variational distributions. In several studies of real and synthetic data, we show that the variance of the estimator of the gradient is significantly lower than other state-of-the-art methods. This leads to faster convergence of stochastic optimization variational inference.”

This talk is part of the Machine Learning @ CUED series.

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