64 citations · 279 across the 30 of their papers we have counts for
9 papers · 1 filter
0/1 Constrained Optimization Solving Sample Average Approximation for Chance Constrained Programming
Shenglong Zhou, Lili Pan, Naihua Xiu +1
Sample average approximation (SAA) is a tractable approach for dealing with chance constrained programming, a challenging stochastic optimization problem. The constraint of SAA is…
Nonconvex quasi-variational inequalities: stability analysis and application to numerical optimization
Joydeep Dutta, Lahoussine Lafhim, Alain Zemkoho +1
We consider a parametric quasi-variational inequality (QVI) without any convexity assumption. Using the concept of \emph{optimal value function}, we transform the problem into that…
Exact Penalty Method for Federated Learning
Shenglong Zhou, and Geoffrey Ye Li
Federated learning has burgeoned recently in machine learning, giving rise to a variety of research topics. Popular optimization algorithms are based on the frameworks of the (stoc…
Gradient Projection Newton Pursuit for Sparsity Constrained Optimization
Shenglong Zhou
Hard-thresholding-based algorithms have seen various advantages for sparse optimization in controlling the sparsity and allowing for fast computation. Recent research shows that wh…
0/1 Deep Neural Networks via Block Coordinate Descent
Hui Zhang, Shenglong Zhou, Geoffrey Ye Li +1
The step function is one of the simplest and most natural activation functions for deep neural networks (DNNs). As it counts 1 for positive variables and 0 for others, its intrinsi…
Test-time Batch Normalization
Tao Yang, Shenglong Zhou, Yuwang Wang +2
Deep neural networks often suffer the data distribution shift between training and testing, and the batch statistics are observed to reflect the shift. In this paper, targeting of…
FedGiA: An Efficient Hybrid Algorithm for Federated Learning
Shenglong Zhou, Geoffrey Ye Li
Federated learning has shown its advances recently but is still facing many challenges, such as how algorithms save communication resources and reduce computational costs, and whet…
Federated Learning via Inexact ADMM
Shenglong Zhou, Geoffrey Ye Li
One of the crucial issues in federated learning is how to develop efficient optimization algorithms. Most of the current ones require full device participation and/or impose strong…
An Oracle Gradient Regularized Newton Method for Quadratic Measurements Regression
Jun Fan, Jie Sun, Ailing Yan +1
Recovering an unknown signal from quadratic measurements has gained popularity due to its wide range of applications, including phase retrieval, fusion frame phase retrieval, and p…