89 citations · 302 across the 30 of their papers we have counts for
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cs.LG2019★ 11 cited
Efficient Privacy-Preserving Stochastic Nonconvex Optimization
Lingxiao Wang, Bargav Jayaraman, David Evans +1
While many solutions for privacy-preserving convex empirical risk minimization (ERM) have been developed, privacy-preserving nonconvex ERM remains a challenge. We study nonconvex E…
stat.ML2019
A Knowledge Transfer Framework for Differentially Private Sparse Learning
Lingxiao Wang, Quanquan Gu
We study the problem of estimating high dimensional models with underlying sparse structures while preserving the privacy of each training example. We develop a differentially priv…
cs.LG2019★ 89 cited
Neural Policy Gradient Methods: Global Optimality and Rates of Convergence
Lingxiao Wang, Qi Cai, Zhuoran Yang +1
Policy gradient methods with actor-critic schemes demonstrate tremendous empirical successes, especially when the actors and critics are parameterized by neural networks. However,…