Oral
Adaptive Regret of Convex and Smooth Functions
Lijun Zhang · Tie-Yan Liu · Zhi-Hua Zhou
We investigate online convex optimization in changing environments, and choose the adaptive regret as the performance measure. The goal is to achieve a small regret over every interval so that the comparator is allowed to change over time. Different from previous works that only utilize the convexity condition, this paper further exploits smoothness to improve the adaptive regret. To this end, we develop novel adaptive algorithms for convex and smooth functions, and establish problem-dependent regret bounds over any interval. Our regret bounds are comparable to existing results in the worst case, and become much tighter when the comparator has a small loss.
Author Information
Lijun Zhang (Nanjing University)
Tie-Yan Liu (Microsoft)
Zhi-Hua Zhou (Nanjing University)
Related Events (a corresponding poster, oral, or spotlight)
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2019 Poster: Adaptive Regret of Convex and Smooth Functions »
Thu Jun 13th 06:30 -- 09:00 PM Room Pacific Ballroom
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