论文标题
最佳比较器自适应在线学习，开关成本
Optimal Comparator Adaptive Online Learning with Switching Cost
论文作者
论文摘要
实际在线学习任务通常是在不受约束的域上自然定义的，在无约束的域中，一般凸损失的最佳算法的特征是比较器适应性的概念。在本文中，我们在开关成本的存在下设计了这样的算法 - 后者惩罚了自适应算法的典型乐观情绪，从而实现了精致的设计权衡。基于通过连续时间分析发现的新型双空间扩展策略，我们提出了一种简单的算法，将现有的比较器自适应后悔绑定[ZCP22A]提高到最佳速率。获得的好处进一步扩展到专家环境，并通过顺序投资任务证明了拟议算法的实用性。
Practical online learning tasks are often naturally defined on unconstrained domains, where optimal algorithms for general convex losses are characterized by the notion of comparator adaptivity. In this paper, we design such algorithms in the presence of switching cost - the latter penalizes the typical optimism in adaptive algorithms, leading to a delicate design trade-off. Based on a novel dual space scaling strategy discovered by a continuous-time analysis, we propose a simple algorithm that improves the existing comparator adaptive regret bound [ZCP22a] to the optimal rate. The obtained benefits are further extended to the expert setting, and the practicality of the proposed algorithm is demonstrated through a sequential investment task.
