Algorithms with Predictions

Abstract

Online algorithms have to answer a sequence of requests without knowing the whole sequence beforehand. We want to survey recent results about how predictions about future requests, given, e.g., by some machine-learning approach, can influence the performance of online algorithms.

Objective

To systematically acquire an overview of the impact of side information on the performance of online algorithms, especially in the context of , e.g., machine-learned, predictions about future requests.

Content

In the classical model of online algorithms, one assumes that the input is revealed piecewise in the form of requests over time and an algorithm has to respond with a part of the output to each request. While there are many situations in which this model is more realistic than the classical model of computation where the whole input is known in advance, not knowing anything about future requests is a quite pessimistic assumption. Recently, several approaches have been introduced to incorporate some kind of predictions about future requests into the model. These predictions can, e,g,, be based on some statistical knowledge about typical instances or can be generated by some machine-learning approaches. In this seminar, after some brief introduction to online algorithms, we want to explore the impact of different kinds of predictions on the solution quality of online algorithms. Each participant will study one aspect of this topic, following a specific scientific publication, and will give a presentation about this topic.

Resources