  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.
