25 research outputs found
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 -DP and -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 is small relative to the sample size . 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 . We then
propose a debiased IPW estimator that achieves the same guarantees in the
regime . 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