Efficient Private ERM for Smooth Objectives

In this paper, we consider efficient differentially private empirical risk minimization from the viewpoint of optimization algorithms. For strongly convex and smooth objectives, we prove that gradient descent with output perturbation not only achieves nearly optimal utility, but also significantly improves the running time of previous state-of-the-art private optimization algorithms, for both $\epsilon$-DP and $(\epsilon, \delta)$-DP. For non-convex but smooth objectives, we propose an RRPSGD (Random Round Private Stochastic Gradient Descent) algorithm, which provably converges to a stationary point with privacy guarantee. Besides the expected utility bounds, we also provide guarantees in high probability form. Experiments demonstrate that our algorithm consistently outperforms existing method in both utility and running time

Understanding the Relationship between Social Media Use and Depression: A Systematic Review

There have been many studies on the relationship between social media use and depression in recent years, but there are inconsistencies between their findings. Using the systematic review method, we analyzed the existing body of work on the relationship between social media use and depression in the information systems field. We selected the Web of Science, Emerald, JSTOR, Science Direct, Taylor & Francis Online and Wiley Online Library as search databases, and ended up with 24 papers that met all our requirements. We identified four possible reasons for the inconsistencies. First, the measurement indicators of social media use are different. Second, depression is not measured in the same way. Third, the studies considered different populations of social media users. Fourth, the mediating factors are different with regards to the relationship between social media use and depression. This study provides literature supported theoretical insights for further exploration and analysis

When is the estimated propensity score better? High-dimensional analysis and bias correction

Anecdotally, using an estimated propensity score is superior to the true propensity score in estimating the average treatment effect based on observational data. However, this claim comes with several qualifications: it holds only if propensity score model is correctly specified and the number of covariates $d$ is small relative to the sample size $n$. We revisit this phenomenon by studying the inverse propensity score weighting (IPW) estimator based on a logistic model with a diverging number of covariates. We first show that the IPW estimator based on the estimated propensity score is consistent and asymptotically normal with smaller variance than the oracle IPW estimator (using the true propensity score) if and only if $n \gtrsim d^2$. We then propose a debiased IPW estimator that achieves the same guarantees in the regime $n \gtrsim d^{3/2}$. Our proofs rely on a novel non-asymptotic decomposition of the IPW error along with careful control of the higher order terms.Comment: Fangzhou Su and Wenlong Mou contributed equally to this wor