Improving Online Algorithms via ML Predictions

被引：0
|
作者：
Kumar, Ravi [1 ]
Purohit, Manish [1 ]
Svitkina, Zoya [1 ]
机构：
[1] Google, Mountain View, CA 94043 USA
来源：
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018) | 2018年 / 31卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this work we study the problem of using machine-learned predictions to improve the 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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页数：10
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