Talk
Conditional Accelerated Lazy Stochastic Gradient Descent
Guanghui · Sebastian Pokutta · Yi Zhou · Daniel Zink
Parkside 2
[
Abstract
]
Abstract:
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).
Live content is unavailable. Log in and register to view live content