Optimal Parameter-free Online Learning with Switching Cost. (arXiv:2205.06846v2 [cs.LG] UPDATED)

Parameter-freeness in online learning refers to the adaptivity of an
algorithm with respect to the optimal decision in hindsight. In this paper, we
design such algorithms in the presence of switching cost - the latter penalizes
the optimistic updates required by parameter-freeness, leading to a delicate
design trade-off. Based on a novel dual space scaling strategy, we propose a
simple yet powerful algorithm for Online Linear Optimization (OLO) with
switching cost, which improves the existing suboptimal regret bound [ZCP22a] to …

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