Learning-Augmented Algorithms with Explicit Predictors

TL;DR

This paper introduces a new approach to online algorithms that integrates learning directly into the algorithmic process, allowing for improved performance in problems like caching and scheduling by designing tailored learning algorithms.

Contribution

It presents a novel framework that incorporates explicit, adaptive learning within online algorithms, moving beyond black-box predictors to optimize problem-specific performance.

Findings

Improved performance bounds for caching and scheduling problems.

Algorithms that learn and adapt during input processing.

Enhanced robustness with theoretical guarantees.

Abstract

Recent advances in algorithmic design show how to utilize predictions obtained by machine learning models from past and present data. These approaches have demonstrated an enhancement in performance when the predictions are accurate, while also ensuring robustness by providing worst-case guarantees when predictions fail. In this paper we focus on online problems; prior research in this context was focused on a paradigm where the predictor is pre-trained on past data and then used as a black box (to get the predictions it was trained for). In contrast, in this work, we unpack the predictor and integrate the learning problem it gives rise for within the algorithmic challenge. In particular we allow the predictor to learn as it receives larger parts of the input, with the ultimate goal of designing online learning algorithms specifically tailored for the algorithmic task at hand. Adopting this perspective, we focus on a number of fundamental problems, including caching and scheduling, which have been well-studied in the black-box setting. For each of the problems we consider, we introduce new algorithms that take advantage of explicit learning algorithms which we carefully design towards optimizing the overall performance. We demonstrate the potential of our approach by deriving performance bounds which improve over those established in previous work.