Bayesian Adversarial Learning
This paper proposes a Bayesian model for adversarial learning problem. Empirical studies on Fashion-MINST and traffic sign recognition show that the proposed methods is slightly better than other adversarial learning baselines.  Below I list my concerns about the paper: For modeling, 1. This paper ignore a highly relevant work ‘Bayesian GAN’ [1]. The non-cooperative game between ‘data generator’ and ‘learner’ established in this paper is almost the same as the vanilla GAN. And the fully Bayesian treatment introduced is exactly the same as in [1]. 2. The author mentioned the posterior inference over learner’s parameter can be achieved by Gibbs sampling on the two defined conditional posterior. The analysis of the underlying joint posterior distribution is missing. It is hard for readers to understand what actually the distribution over learner is achieved when the algorithm converges. 3. It also remains unclear that whether the iteration introduced in Eqn. (3) and (4) is guaranteed to converge.  For inference, 1. In the consideration of the scalability, the author proposed to only update a mini-batch of training data points. However, no theoretical insight is provided to validate this stochastic approximation. Does this stochastic process on expectation match the iteration defined in Eqn. (3) and (4)?  For experiments, 1. In section 5, the author suggests a sample size of 5 and claims that better performance can be achieved with a larger sample size. I recommend including results with different sample size to support the augment.   Overall, I think this is an interesting paper exploring the functionals of Bayesian modeling in the context of adversarial learning. However, the novelty of proposed method is limited, and the relationship between the current work and [1] is totally ignored in the paper. I recommend weak rejection for this paper.  [1] Bayesian GAN; Yunus Saatchi, Andrew Gordon Wilsons ----------------------------- Update: I have read the author feedback and I appreciate the authors’ reply on my concerns.   I believe given the previous work ‘Bayesian GAN’, another way to frame the contribution would be adapting the techniques in Bayesian GAN for the problem of adversarial attacks. In terms of methodology, the only main difference is that Bayesian GAN generates samples conditioned on random noise while BAL generates samples conditioned on the input data (as mentioned in the first point of the author feedback). The other parts are very similar.  The application itself is important and interesting. I am happy to see the Bayesian principle help design a good adversarial defense framework that achieves good empirical results.  Based on the reasons above, I will slightly upgrade my score (from 5 to 6).  If this paper is accepted, I hope my following comments about *target posterior distribution*  can be addressed in the final version.  Surely L88-L89 gives a formula of the parameter posterior. However I urge authors to articulate what is the conditional distribution p(\tilde{D} |D). Since in the previous text, only p(\tilde{D} |\theta, D) is defined. It is very important to have a clear idea about what is the target of the learning algorithm. Missing this critical part may confuse the reader.  For the inference part, giving a verification of mini-batch sampling as the authors suggested in the rebuttal will also make this paper more self-contained.