A burgeoning paradigm in algorithm design is the field of algorithms with predictions, in
which algorithms can take advantage of a possibly-imperfect prediction of some aspect of …

Augmenting algorithms with learned predictions is a promising approach for going beyond
worst-case bounds. Dinitz, Im, Lavastida, Moseley, and Vassilvitskii~(2021) have …

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 …

Semidefinite programming (SDP) is a unifying framework that generalizes both linear
programming and quadratically-constrained quadratic programming, while also yielding …

When applying differential privacy to sensitive data, a common way of getting improved
performance is to use external information such as other sensitive data, public data, or …

We consider the problem of clustering in the learning-augmented setting, where we are
given a data set in $ d $-dimensional Euclidean space, and a label for each data point given …

There is significant interest in deploying machine learning algorithms for diagnostic
radiology, as modern learning techniques have made it possible to detect abnormalities in …

We study learning-augmented binary search trees (BSTs) and B-Trees via Treaps with
composite priorities. The result is a simple search tree where the depth of each item is …

In today's rapidly evolving, technology-driven and data-rich environment, we are
increasingly being offered new information with which to make decisions. This dissertation …