Daniel Zink

2 accepted papers

2017

Conditional Accelerated Lazy Stochastic Gradient Descent

Guanghui Lan, Sebastian Pokutta, Yi Zhou, Daniel Zink

ICML 2017poster

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…

Cited by 45 Source ↗PDF ↗Scholar ↗

2017

Lazifying Conditional Gradient Algorithms

Gábor Braun, Sebastian Pokutta, Daniel Zink

ICML 2017poster

Conditional gradient algorithms (also often called Frank-Wolfe algorithms) are popular due to their simplicity of only requiring a linear optimization oracle and more recently they also gained significant traction for online learning. While simple in principle, in many cases the actual implementatio…

Cited by 61 Source ↗PDF ↗Scholar ↗