Emergent Mind
Online Non-Convex Learning: Following the Perturbed Leader is Optimal
(1903.08110)
Published Mar 19, 2019
in
cs.LG
,
math.OC
,
and
stat.ML
Abstract
We study the problem of online learning with non-convex losses, where the learner has access to an offline optimization oracle. We show that the classical Follow the Perturbed Leader (FTPL) algorithm achieves optimal regret rate of $O(T{-1/2})$ in this setting. This improves upon the previous best-known regret rate of $O(T{-1/3})$ for FTPL. We further show that an optimistic variant of FTPL achieves better regret bounds when the sequence of losses encountered by the learner is `predictable'.
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