Designing online algorithms with machine learning predictions is a recent technique beyond the worst-case paradigm for various practically relevant online problems (scheduling, caching, clustering, ski rental, etc.). While most previous learning-augmented algorithm approaches focus on integrating the predictions of a single oracle, we study the design of online algorithms with \emph{multiple} experts. To go beyond the popular benchmark of a static best expert in hindsight, we propose a new \emph{dynamic} benchmark (linear combinations of predictions that change over time). We present a competitive algorithm in the new dynamic benchmark with a performance guarantee of $O(\log K)$, where $K$ is the number of experts, for $0-1$ online optimization problems. Furthermore, our multiple-expert approach provides a new perspective on how to combine in an online manner several online algorithms - a long-standing central subject in the online algorithm research community.

翻译：利用机器学习预测设计在线算法是近年来针对各种实际相关在线问题（调度、缓存、聚类、滑雪租赁等）超越最坏情况范式的一种技术手段。以往大多数学习增强型算法方法侧重于整合单一预言机的预测，而我们则研究基于多专家进行在线算法设计的新方向。为了超越经典"事后静态最优专家"基准，我们提出了一种新的动态基准（随时间变化的预测线性组合）。针对0-1在线优化问题，我们提出了一种在新动态基准下具有$O(\log K)$性能保证的竞争算法，其中$K$为专家数量。此外，我们的多专家方法为在线组合多个在线算法这一长期困扰在线算法研究领域的核心问题提供了新的解决视角。