Traditional online algorithms encapsulate decision making under uncertainty, and give ways to hedge against all possible future events, while guaranteeing a nearly optimal solution, as compared to an offline optimum. On the other hand, machine learning algorithms are in the business of extrapolating patterns found in the data to predict the future, and usually come with strong guarantees on the expected generalization error. In this work, we develop a framework for augmenting online algorithms with a machine learned predictor to achieve competitive ratios that provably improve upon unconditional worst-case lower bounds when the predictor has low error. Our approach treats the predictor as a complete black box and is not dependent on its inner workings or the exact distribution of its errors. We apply this framework to the traditional caching problem—creating an eviction strategy for a cache of size k . We demonstrate that naively following the oracle’s recommendations may lead to very poor performance, even when the average error is quite low. Instead, we show how to modify the Marker algorithm to take into account the predictions and prove that this combined approach achieves a competitive ratio that both (i) decreases as the predictor’s error decreases and (ii) is always capped by O (log k ), which can be achieved without any assistance from the predictor. We complement our results with an empirical evaluation of our algorithm on real-world datasets and show that it performs well empirically even when using simple off-the-shelf predictions.

中文翻译：

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具有机器学习建议的竞争性缓存

与离线最优相比，传统的在线算法封装了不确定性下的决策，并提供了对冲所有可能的未来事件的方法，同时保证了接近最优的解决方案。另一方面，机器学习算法的业务是推断数据中发现的模式以预测未来，并且通常对预期的泛化误差提供强有力的保证。在这项工作中，我们开发了一个框架，用于使用机器学习的预测器来增强在线算法，以实现竞争比率，当预测器具有低误差时，可证明该比率可提高无条件的最坏情况下限。我们的方法将预测器视为一个完整的黑匣子，不依赖于其内部运作或其误差的确切分布。ķ. 我们证明，天真地遵循预言机的建议可能会导致性能非常差，即使平均错误非常低。相反，我们展示了如何修改 Marker 算法以将预测考虑在内，并证明这种组合方法实现了竞争比率，即 (i) 随着预测器误差的减小而降低，并且 (ii) 始终由○（日志ķ)，这可以在没有预测器的任何帮助的情况下实现。我们通过在现实世界数据集上对我们的算法进行经验评估来补充我们的结果，并表明即使使用简单的现成预测，它在经验上也表现良好。