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.
arXiv comments
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
This is a wonderful paper. In a similar vein, "Algorithms with Predictions" (https://arxiv.org/pdf/2006.09123.pdf) presents a bunch of fun algorithmic problems related to bringing AI into traditional algorithms.
The field of "algorithms with predictions" studies how to use predictions/learning within traditional CS problem settings: > We introduce algorithms that use predictions from machine learning applied to the input to circumvent worst-case analysis. We aim for algorithms that have…
This is a wonderful paper. In a similar vein, "Algorithms with Predictions" (https://arxiv.org/pdf/2006.09123.pdf) presents a bunch of fun algorithmic problems related to bringing AI into traditional algorithms.
morkpekView on HNParent context
The field of "algorithms with predictions" studies how to use predictions/learning within traditional CS problem settings: > We introduce algorithms that use predictions from machine learning applied to the input to circumvent worst-case analysis. We aim for algorithms that have…
lqrView on HNParent context