146 citations · 374 across the 21 of their papers we have counts for
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math.OC2019
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…
math.OC2019★ 31 cited
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…
math.OC2019★ 146 cited
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…