Paper:
Generalization error (also known as the out-of-sample error) measures how well the hypothesis learned from training data generalizes to previously unseen data. Proving tight generalization error bounds is a central question in statistical learning theory. In this paper, we obtain generalization error bounds for learning general non-convex objectives, which has attracted significant attention in recent years. We develop a new framework, termed Bayes-Stability, for proving algorithm-dependent generalization error bounds. The new framework combines ideas from both the PAC-Bayesian theory and the notion of algorithmic stability. Applying the Bayes-Stability method, we obtain new data-dependent generalization bounds for stochastic gradient Langevin dynamics (SGLD) and several other noisy gradient methods (e.g., with momentum, mini-batch and acceleration, Entropy-SGD). Our result recovers (and is typically tighter than) a recent result in Mou et al. (2018)  and improves upon the results in Pensia et al. (2018). Our experiments demonstrate that our data-dependent bounds can distinguish randomly labelled data from normal data, which provides an explanation to the intriguing phenomena observed in Zhang et al. (2017a). We also study the setting where the total loss is the sum of a bounded loss and an additional 2 regularization term. We obtain new generalization bounds for the continuous Langevin dynamic in this setting by developing a new Log-Sobolev inequality for the parameter distribution at any time. Our new bounds are more desirable when the noise level of the process is not very small, and do not become vacuous even when T tends to infinity.

Review:
This paper aims at developing a better understanding of generalization error for increasingly prevalent non-convex learning problems. For many such problems, the existing generalization bounds in the statistical learning theory literature are not very informative. To address these issues, the paper explores algorithm-specific generalization bounds,  especially focusing on various types of noisy gradient methods.
The paper employs a framework that combines uniform stability and PAC-Bayesian theory to obtain generalization bound for the noisy gradient methods. For gradient Langevin dynamic (GLD) and stochastic gradient Langevin dynamics (SGLD), using this Bayes-Stability framework, the paper obtains a generalization bound on the expected generalization error that scales with the expected empirical squared gradient norm. As argued in the paper, this provides an improvement over the existing bounds in the literature. Furthermore, this bound enables the treatment of the setting with noisy labels. For this setting the expected empirical squared gradient norm along the optimization path is higher, leading to worse generalization bound. 
The paper then extends their results to the setting where an $\ell_2$ regularization is added to the non-convex objective. By using a new Log-Sobolev inequality for the parameter distribution at time t, the paper obtains new generalization bounds for continuous Langevin dynamic (CLD). These bounds subsequently provide bounds for GLD as well.
The paper demonstrates the utility of their generalization bound via empirical evaluation on MNIST and CIFAR dataset. The obtained generalization bounds are informative as they appear to capture the trend in the generalization error. 
Overall, the paper is very well written with a clear comparison with the existing generalization bounds. The results in the paper are interesting and novel. That said, the discussion in the introduction and abstract appears a bit misleading as it gives the impression that this is the first paper that combines the ideas from stability and PAC-Bayesian theory to obtain generalization bounds. This is not the case, e.g. see [1].
As noted by the authors, some of the bounds obtained in this paper share similarities with one of the bounds in Mou et al.  as all these bounds contain the expected empirical squared gradient norm. The bound in Mou et al. holds with high probability and decays as $O(1/\sqrt{n})$, whereas the bounds in this paper are on expected generalization error and decay as $O(1/n)$. Could authors comment on extending their results to hold with high probability and how it would affect their bounds?
[1] Rivasplata et al., PAC-Bayes bounds for stable algorithms with instance-dependent priors.
----------------------- Post author response -------------
Thank you for addressing my comments. I have decided to keep my original score unchanged.