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Poster
Online Convex Optimization with Unconstrained Domains and Losses
Ashok Cutkosky · Kwabena A Boahen
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.
Author Information
Ashok Cutkosky (Stanford University)
Kwabena A Boahen (Stanford University)
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