18 citations · 36 across the 7 of their papers we have counts for
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cs.LG2020
Differentially Private ADMM Algorithms for Machine Learning
Tao Xu, Fanhua Shang, Yuanyuan Liu +3
In this paper, we study efficient differentially private alternating direction methods of multipliers (ADMM) via gradient perturbation for many machine learning problems. For smoot…
cs.LG2019★ 1 cited
Efficient Relaxed Gradient Support Pursuit for Sparsity Constrained Non-convex Optimization
Fanhua Shang, Bingkun Wei, Hongying Liu +2
Large-scale non-convex sparsity-constrained problems have recently gained extensive attention. Most existing deterministic optimization methods (e.g., GraSP) are not suitable for l…
cs.LG2018★ 13 cited
VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning
Fanhua Shang, Kaiwen Zhou, Hongying Liu +5
In this paper, we propose a simple variant of the original SVRG, called variance reduced stochastic gradient descent (VR-SGD). Unlike the choices of snapshot and starting points in…