  We study the integration of machine learning advice into the design of skip
lists to improve upon traditional data structure design. Given access to a
possibly erroneous oracle that outputs estimated fractional frequencies for
search queries on a set of items, we construct a skip list that provably
provides the optimal expected search time, within nearly a factor of two. In
fact, our learning-augmented skip list is still optimal up to a constant
factor, even if the oracle is only accurate within a constant factor. We show
that if the search queries follow the ubiquitous Zipfian distribution, then the
expected search time for an item by our skip list is only a constant,
independent of the total number $n$ of items, i.e., $\mathcal{O}(1)$, whereas a
traditional skip list will have an expected search time of $\mathcal{O}(\log
n)$. We also demonstrate robustness by showing that our data structure achieves
an expected search time that is within a constant factor of an oblivious skip
list construction even when the predictions are arbitrarily incorrect. Finally,
we empirically show that our learning-augmented skip list outperforms
traditional skip lists on both synthetic and real-world datasets.
