Improving Online Algorithms Via ML Predictions

Authors:
Manish Purohit Google
Zoya Svitkina Google
Ravi Kumar Google

Introduction:

In this work the authors study the problem of using machine-learned predictions to improve performance of online algorithms.

Abstract:

In this work we study the problem of using machine-learned predictions to improve performance of online algorithms. We consider two classical problems, ski rental and non-clairvoyant job scheduling, and obtain new online algorithms that use predictions to make their decisions. These algorithms are oblivious to the performance of the predictor, improve with better predictions, but do not degrade much if the predictions are poor.

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