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Poster
Conditional Accelerated Lazy Stochastic Gradient Descent
Guanghui · Sebastian Pokutta · Yi Zhou · Daniel Zink

Tue Aug 08 01:30 AM -- 05:00 AM (PDT) @ Gallery #40

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).

Author Information

Guanghui (George)
Sebastian Pokutta (Georgia Tech)
Yi Zhou (Georgia Institute of Technology)
Daniel Zink

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