Parameter-Free Online Convex Optimization with Sub-Exponential Noise

5 Feb 2019Kwang-Sung JunFrancesco Orabona

We consider the problem of unconstrained online convex optimization (OCO) with sub-exponential noise, a strictly more general problem than the standard OCO. In this setting, the learner receives a subgradient of the loss functions corrupted by sub-exponential noise and strives to achieve optimal regret guarantee, without knowledge of the competitor norm, i.e., in a parameter-free way... (read more)

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