Learning-Augmented Query Policies

TL;DR

This paper explores how to leverage machine-learned predictions to reduce query complexity in algorithms solving problems under uncertainty, providing performance guarantees that improve with prediction accuracy.

Contribution

It introduces algorithms that utilize predictions to minimize queries in uncertain data problems, with robustness and PAC learnability analysis.

Findings

Algorithms with performance improving with prediction accuracy

Robust algorithms effective even with poor predictions

Experimental validation confirms theoretical results

Abstract

We study how to utilize (possibly machine-learned) predictions in a model for computing under uncertainty in which an algorithm can query unknown data. The goal is to minimize the number of queries needed to solve the problem. We consider fundamental problems such as finding the minima of intersecting sets of elements or sorting them (these problems can also be phrased as (hyper)graph orientation problems), as well as the minimum spanning tree problem. We discuss different measures for the prediction accuracy and design algorithms with performance guarantees that improve with the accuracy of predictions and that are robust with respect to very poor prediction quality. These measures are intuitive and might be of general interest for inputs involving uncertainty intervals. We show that our predictions are PAC learnable. We also provide new structural insights for the minimum spanning tree problem that might be useful in the context of explorable uncertainty regardless of predictions. Our results prove that untrusted predictions can circumvent known lower bounds in the model of explorable uncertainty. We complement our results by experiments that empirically confirm the performance of our algorithms.