Web: http://arxiv.org/abs/2205.01921

May 5, 2022, 1:12 a.m. | Dheeraj Baby, Yu-Xiang Wang

cs.LG updates on arXiv.org arxiv.org

We consider the problem of universal dynamic regret minimization under
exp-concave and smooth losses. We show that appropriately designed Strongly
Adaptive algorithms achieve a dynamic regret of $\tilde O(d^2 n^{1/5} C_n^{2/5}
\vee d^2)$, where $n$ is the time horizon and $C_n$ a path variational based on
second order differences of the comparator sequence. Such a path variational
naturally encodes comparator sequences that are piecewise linear -- a powerful
family that tracks a variety of non-stationarity patterns in practice (Kim et …

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