146 citations · 374 across the 21 of their papers we have counts for
21 papers
Online Primal-Dual Mirror Descent under Stochastic Constraints
Xiaohan Wei, Hao Yu, Michael J. Neely
We consider online convex optimization with stochastic constraints where the objective functions are arbitrarily time-varying and the constraint functions are independent and ident…
On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization
Hao Yu, Rong Jin
For SGD based distributed stochastic optimization, computation complexity, measured by the convergence rate in terms of the number of stochastic gradient calls, and communication c…
On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization
Hao Yu, Rong Jin, Sen Yang
Recent developments on large-scale distributed machine learning applications, e.g., deep neural networks, benefit enormously from the advances in distributed non-convex optimizatio…
Solving Non-smooth Constrained Programs with Lower Complexity than : A Primal-Dual Homotopy Smoothing Approach
Xiaohan Wei, Hao Yu, Qing Ling +1
We propose a new primal-dual homotopy smoothing algorithm for a linearly constrained convex program, where neither the primal nor the dual function has to be smooth or strongly con…
Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning
Hao Yu, Sen Yang, Shenghuo Zhu
In distributed training of deep neural networks, parallel mini-batch SGD is widely used to speed up the training process by using multiple workers. It uses multiple workers to samp…
Fast K-Means Clustering with Anderson Acceleration
Juyong Zhang, Yuxin Yao, Yue Peng +2
We propose a novel method to accelerate Lloyd's algorithm for K-Means clustering. Unlike previous acceleration approaches that reduce computational cost per iterations or improve i…