Estimating Gradients for Discrete Random Variables by Sampling without Replacement
This work addresses variance reduction in gradient estimation for discrete variables, which is incremental as it builds on existing estimators like REINFORCE.
The paper tackles the problem of estimating gradients for discrete random variables by introducing an unbiased estimator based on sampling without replacement, which reduces variance by avoiding duplicate samples. Experiments show it consistently performs among the best in both high and low entropy settings.
We derive an unbiased estimator for expectations over discrete random variables based on sampling without replacement, which reduces variance as it avoids duplicate samples. We show that our estimator can be derived as the Rao-Blackwellization of three different estimators. Combining our estimator with REINFORCE, we obtain a policy gradient estimator and we reduce its variance using a built-in control variate which is obtained without additional model evaluations. The resulting estimator is closely related to other gradient estimators. Experiments with a toy problem, a categorical Variational Auto-Encoder and a structured prediction problem show that our estimator is the only estimator that is consistently among the best estimators in both high and low entropy settings.