activity
20162020
most citedFast and Secure Distributed Nonnegative Matrix Factorization

10 citations · 14 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG202010 cited

Fast and Secure Distributed Nonnegative Matrix Factorization

Yuqiu Qian, Conghui Tan, Danhao Ding +2

Nonnegative matrix factorization (NMF) has been successfully applied in several data mining tasks. Recently, there is an increasing interest in the acceleration of NMF, due to its…

math.OC20204 cited

Accelerated Dual-Averaging Primal-Dual Method for Composite Convex Minimization

Conghui Tan, Yuqiu Qian, Shiqian Ma +1

Dual averaging-type methods are widely used in industrial machine learning applications due to their ability to promoting solution structure (e.g., sparsity) efficiently. In this p…

cs.LG2019

Central Server Free Federated Learning over Single-sided Trust Social Networks

Chaoyang He, Conghui Tan, Hanlin Tang +2

Federated learning has become increasingly important for modern machine learning, especially for data privacy-sensitive scenarios. Existing federated learning mostly adopts the cen…

math.OC2018

Stochastic Primal-Dual Method for Empirical Risk Minimization with Per-Iteration Complexity

Conghui Tan, Tong Zhang, Shiqian Ma +1

Regularized empirical risk minimization problem with linear predictor appears frequently in machine learning. In this paper, we propose a new stochastic primal-dual method to solve…

math.OC2016

Barzilai-Borwein Step Size for Stochastic Gradient Descent

Conghui Tan, Shiqian Ma, Yu-Hong Dai +1

One of the major issues in stochastic gradient descent (SGD) methods is how to choose an appropriate step size while running the algorithm. Since the traditional line search techni…