Poster
Natasha 2: Faster Non-Convex Optimization Than SGD
Zeyuan Allen-Zhu
Room 210 #50
Keywords: [ Learning Theory ] [ Non-Convex Optimization ]
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Abstract
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Abstract:
We design a stochastic algorithm to find $\varepsilon$-approximate local minima of any smooth nonconvex function in rate $O(\varepsilon^{-3.25})$, with only oracle access to stochastic gradients. The best result before this work was $O(\varepsilon^{-4})$ by stochastic gradient descent (SGD).
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