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Poster
Online Convex Optimization with Unconstrained Domains and Losses
Ashok Cutkosky · Kwabena A Boahen

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #76 #None

We propose an online convex optimization algorithm (RescaledExp) that achieves optimal regret in the unconstrained setting without prior knowledge of any bounds on the loss functions. We prove a lower bound showing an exponential separation between the regret of existing algorithms that require a known bound on the loss functions and any algorithm that does not require such knowledge. RescaledExp matches this lower bound asymptotically in the number of iterations. RescaledExp is naturally hyperparameter-free and we demonstrate empirically that it matches prior optimization algorithms that require hyperparameter optimization.