Paper:
DOUBLY REPARAMETERIZED GRADIENT ESTIMATORS FOR MONTE CARLO OBJECTIVES
Deep latent variable models have become a popular model choice due to the scalable learning algorithms introduced by (Kingma & Welling, 2013;Rezende et al., 2014). These approaches maximize a variational lower bound on the intractable log likelihood of the observed data. Burda et al. (2015) introduced a multi-sample variational bound, IWAE, that is at least as tight as the standard variational lower bound and becomes increasingly tight as the number of samples increases. Counterintuitively, the typical inference network gradient estimator for the IWAE bound performs poorly as the number of samples increases (Rainforth et al., 2018;. Roeder et al. (2017) propose an improved gradient estimator, however, are unable to show it is unbiased. We show that it is in fact biased and that the bias can be estimated efficiently with a second application of the reparameterization trick. The doubly reparameterized gradient (DReG) estimator does not suffer as the number of samples increases, resolving the previously raised issues. The same idea can be used to improve many recently introduced training techniques for latent variable models. In particular, we show that this estimator reduces the variance of the IWAE gradient, the reweighted wake-sleep update (RWS) (Bornschein & Bengio, 2014), and the jackknife variational inference (JVI) gradient (Nowozin, 2018). Finally, we show that this computationally efficient, unbiased drop-in gradient estimator translates to improved performance for all three objectives on several modeling tasks.

Review:
Overall:
This paper works on improving the gradient estimator of the ELBO. Author experimentally found that the estimator of the existing work(STL) is biased and proposed to reduce the bias by using the technique like  REINFORCE.
The problem author focused on is unique and the solution is simple, experiments show that proposed method seems promising.
Clarity:
The paper is clearly written in the sense that the motivation of research is clear, the derivation of the proposed method is easy to understand.
Significance:
I think this kind of research makes the variational inference more useful, so this work is significant. But I cannot tell the proposed method is really useful, so I gave this score.
The reason I doubt the reason is that as I written in the below, the original STL can handle the mixture of Gaussians as the latent variable but the proposed method cannot. So I do not know which is better and whether I should use this method or use the original STL with flexible posterior distribution to tighten the evidence lower bound. I think additional experiments are needed. I know that motivation is a bit different for STL and proposed method but some comparisons are needed.
Question and minor comments:
In the original paper of STL, the author pointed out that by freezing the gradient of variational parameters to drop the score function term, we can utilize the flexible variational families like the mixture of Gaussians.
In this work, since we do not freeze the variational parameters, we cannot utilize the mixture of Gaussians as in the STL. IWAE improves the lower bound by increasing the samples, but we can also improve the bound by specifying the flexible posteriors like the mixture of Gaussians in STL.
Faced on this, I wonder which strategy is better to tighten the lower bound, should we use the STL with the mixture of Gaussians or use the proposed method?  
To clarify the usefulness of this method, I think the additional experimental comparisons are needed.
About the motivation of the paper, I think it might be better to move the Fig.1 about the Bias to the introduction and clearly state that the author found that the STL is biased "experimentally".
Followings are minor comments.
In experiment 6.1, I'm not sure why the author present the result of K ELBO estimator in the plot of Bias and Variance.
I think author want to point that when K=1, STL is unbiased with respect to the 1 ELBO, but when k>1, it is biased with respect to IWAE estimator.
However, the objective of K ELBO and IWAE are different, it may be misleading. So this should be noted in the paper.
In Figure 3, the left figure, what each color means? Is the color assignment is the same with the middle figure?
(Same for Figure 4)