Algorithms with Predictions

Abstract

We introduce algorithms that use predictions from machine learning applied to the input to circumvent worst-case analysis. We aim for algorithms that have near optimal performance when these predictions are good, but recover the prediction-less worst case behavior when the predictions have large errors.

survey is to appear as a chapter in Beyond the Worst-Case Analysis of Algorithms, a collection edited by Tim Roughgarden. We hope to occasionally update the survey here, with new versions that include discussions of new results and advances in the area of Algorithms with Predictions