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Recent effort to test deep learning systems has produced an intuitive and compelling test criterion called neuron coverage (NC), which resembles the notion of traditional code coverage. NC measures the proportion of neurons activated in a neural network and it is implicitly assumed that increasing NC improves the quality of a test suite. In an attempt to automatically generate a test suite that increases NC, we design a novel diversity promoting regularizer that can be plugged into existing adversarial attack algorithms. We then assess whether such attempts to increase NC could generate a test suite that (1) detects adversarial attacks successfully, (2) produces natural inputs, and (3) is unbiased to particular class predictions. Contrary to expectation, our extensive evaluation finds that increasing NC actually makes it harder to generate an effective test suite: higher neuron coverage leads to fewer defects detected, less natural inputs, and more biased prediction preferences. Our results invoke skepticism that increasing neuron coverage may not be a meaningful objective for generating tests for deep neural networks and call for a new test generation technique that considers defect detection, naturalness, and output impartiality in tandem. 

We consider the differentially private sparse learning problem, where the goal is to estimate the underlying sparse parameter vector of a statistical model in the high-dimensional regime while preserving the privacy of each training example. We propose a generic differentially private iterative gradient hard threshoding algorithm with a linear convergence rate and strong utility guarantee. We demonstrate the superiority of our algorithm through two specific applications: sparse linear regression and sparse logistic regression. Specifically, for sparse linear regression, our algorithm can achieve the best known utility guarantee without any extra support selection procedure used in previous work [Kifer et al., 2012]. For sparse logistic regression, our algorithm can obtain the utility guarantee with a logarithmic dependence on the problem dimension. Experiments on both synthetic data and real world datasets verify the effectiveness of our proposed algorithm.