Figure 1: Random samples drawn from Exemplar V AEs trained on different datasets. Exemplar V AE are generated and shown. Define Cache: initialize cache = [] insert( i, c): insert value c with index i into cache update( i, c): update the value of index i to c kNN(c): return indices of kNNs of c in cache for n in {1,...,N } do Cache.insert( Table 1: The number of active dimensions computed based on a metric proposed by Burda et. The exemplar V AE generates a new sample by stochastically transforming an exemplar.

We are very grateful to all reviewers for their detailed, insightful, and constructive comments and questions. But we believe that they are very important, and we will pursue them in our ongoing study. The column "FC" is excerpted from Our responses (blue) to reviewers' comments/questions ( black/bold/italic) are as follows. We will refine our claims, and also refer to these SA VI methods. It turns out that it was our faulty claim.

Decision making is a process that is extremely prone to different biases. In this paper we consider learning fair representations that aim at removing nuisance (sensitive) information from the decision process. For this purpose, we propose to use deep generative modeling and adapt a hierarchical Variational Auto-Encoder to learn these fair representations. Moreover, we utilize the mutual information as a useful regularizer for enforcing fairness of a representation. In experiments on two benchmark datasets and two scenarios where the sensitive variables are fully and partially observable, we show that the proposed approach either outperforms or performs on par with the current best model.