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cs.LG2024
An Equivalence Between Static and Dynamic Regret Minimization
Andrew Jacobsen, Francesco Orabona
We study the problem of dynamic regret minimization in online convex optimization, in which the objective is to minimize the difference between the cumulative loss of an algorithm…
cs.LG2024★ 1 cited
Online Linear Regression in Dynamic Environments via Discounting
Andrew Jacobsen, Ashok Cutkosky
We develop algorithms for online linear regression which achieve optimal static and dynamic regret guarantees \emph{even in the complete absence of prior knowledge}. We present a n…