We propose a novel strategy for extracting features in supervised learning that can be used to construct a classifier which is more robust to small perturbations in the input space. Our method builds upon the idea of the information bottleneck, by introducing an additional penalty term that encourages the Fisher information of the extracted features to be small when parametrized by the inputs. We present two formulations where the relevance of the features to output labels is measured using either mutual information or MMSE. By tuning the regularization parameter, we can explicitly trade off the opposing desiderata of robustness and accuracy when constructing a classifier. We derive optimal solutions to both robust information bottleneck formulations when the inputs and outputs are jointly Gaussian, proving that the optimally robust features are also jointly Gaussian in this setting. We also propose methods for optimizing variational bounds on the robust information bottleneck objectives in general settings using stochastic gradient descent, which may be implemented efficiently in neural networks. Our experimental results for synthetic and real data sets show that the proposed feature extraction methods indeed produce classifiers with increased robustness to perturbations.
