  The research area of algorithms with predictions has seen recent success
showing how to incorporate machine learning into algorithm design to improve
performance when the predictions are correct, while retaining worst-case
guarantees when they are not. Most previous work has assumed that the algorithm
has access to a single predictor. However, in practice, there are many machine
learning methods available, often with incomparable generalization guarantees,
making it hard to pick a best method a priori. In this work we consider
scenarios where multiple predictors are available to the algorithm and the
question is how to best utilize them.
  Ideally, we would like the algorithm's performance to depend on the quality
of the best predictor. However, utilizing more predictions comes with a cost,
since we now have to identify which prediction is the best. We study the use of
multiple predictors for a number of fundamental problems, including matching,
load balancing, and non-clairvoyant scheduling, which have been well-studied in
the single predictor setting. For each of these problems we introduce new
algorithms that take advantage of multiple predictors, and prove bounds on the
resulting performance.
