Poster
Conditional Accelerated Lazy Stochastic Gradient Descent
Guanghui · Sebastian Pokutta · Yi Zhou · Daniel Zink
In this work we introduce a conditional accelerated lazy stochastic gradient descent algorithm with optimal number of calls to a stochastic first-order oracle and convergence rate O(1 / epsilon^2) improving over the projection-free, Online Frank-Wolfe based stochastic gradient descent of (Hazan and Kale, 2012) with convergence rate O(1 / epsilon^4).
Summary/Notes »Author Information
Guanghui (George)
Sebastian Pokutta (Georgia Tech)
Yi Zhou (Georgia Institute of Technology)
Daniel Zink
Related Events (a corresponding poster, oral, or spotlight)
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2017 Talk: Conditional Accelerated Lazy Stochastic Gradient Descent »
Tue Aug 8th 10:48 -- 11:06 AM Room Parkside 2
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