State-of-the-art algorithms for solving hard computational problems often expose many
parameters whose settings critically affect empirical performance. Manually exploring the …

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

Though empirical testing is broadly used to evaluate heuristics, there are shortcomings with
how it is often applied in practice. In a systematic review of Max-Cut and quadratic …

It is well established that in many scenarios there is no single solver that will provide optimal
performance across a wide range of problem instances. Taking advantage of this …

While multitask representation learning has become a popular approach in reinforcement
learning (RL), theoretical understanding of why and when it works remains limited. This …