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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 intri
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