Poster
ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables
Mingzhang Yin · Yuguang Yue · Mingyuan Zhou

Thu Jun 13th 06:30 -- 09:00 PM @ Pacific Ballroom #85
in Posters Thu »

To address the challenge of backpropagating the gradient through categorical variables, we propose the augment-REINFORCE-swap-merge (ARSM) gradient estimator that is unbiased and has low variance. ARSM first uses variable augmentation, REINFORCE, and Rao-Blackwellization to re-express the gradient as an expectation under the Dirichlet distribution, then uses variable swapping to construct differently expressed but equivalent expectations, and finally shares common random numbers between these expectations to achieve significant variance reduction. Experimental results show ARSM closely resembles the performance of the true gradient for optimization in univariate settings; outperforms existing estimators by a large margin when applied to categorical variational auto-encoders; and provides a "try-and-see self-critic" variance reduction method for discrete-action policy gradient, which removes the need of estimating baselines by generating a random number of pseudo actions and estimating their action-value functions.

Keywords: Approximate Inference · Bayesian Deep Learning · Bayesian Methods · Monte Carlo Methods · Optimization

Author Information

Mingzhang Yin (University of Texas at Austin)
Yuguang Yue (University of Texas at Austin)
Mingyuan Zhou (University of Texas at Austin)

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