146 citations · 286 across the 7 of their papers we have counts for
7 papers
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…
Online Learning in Weakly Coupled Markov Decision Processes: A Convergence Time Study
Xiaohan Wei, Hao Yu, Michael J. Neely
We consider multiple parallel Markov decision processes (MDPs) coupled by global constraints, where the time varying objective and constraint functions can only be observed after t…
Online Convex Optimization with Stochastic Constraints
Hao Yu, Michael J. Neely, Xiaohan Wei
This paper considers online convex optimization (OCO) with stochastic constraints, which generalizes Zinkevich's OCO over a known simple fixed set by introducing multiple stochasti…
A Primal-Dual Parallel Method with Convergence for Constrained Composite Convex Programs
Hao Yu, Michael J. Neely
This paper considers large scale constrained convex (possibly composite and non-separable) programs, which are usually difficult to solve by interior point methods or other Newton-…
Online Convex Optimization with Time-Varying Constraints
Michael J. Neely, Hao Yu
This paper considers online convex optimization with time-varying constraint functions. Specifically, we have a sequence of convex objective functions a…