From Switching to Dynamic Regret: A Simple Reduction via Unbiased Random Sequences
In non-stationary online learning, dynamic regret has attracted increasing attention as a measure of how well an online learner performs against a time-varying comparator sequence. Despite considerable advances, attaining optimal bounds for strongly convex and exp-concave losses often involves intricate analysis. In this paper, we present a \textit{simple} framework that reduces dynamic regret minimization to switching regret minimization. As a result, we can derive dynamic regret bounds by using off-the-shelf algorithms with switching regret guarantees. The key idea of our reduction is to construct, for \textit{any} comparator sequence, an auxiliary random sequence that is unbiased at ea...
| Authors | Yibo Wang, Wenhao Yang, Sifan Yang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 17 September 2026 |
| Last updated at source | 1 October 2026 |
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