This paper proposes a new model for augmenting algorithms with useful predictions that go beyond worst-case bounds on the algorithm performance. By refining existing models, our model ensures predictions are formally learnable and instance robust. Learnability guarantees that predictions can be efficiently constructed from past data. Instance robustness formally ensures a prediction is robust to modest changes in the problem input. Further, the robustness model ensures two different predictions can be objectively compared, addressing a shortcoming in prior models. This paper establishes the existence of predictions which satisfy these properties. The paper considers online algorithms with predictions for a network flow allocation problem and the restricted assignment makespan minimization problem. For both problems, three key properties are established: existence of useful predictions that give near optimal solutions, robustness of these predictions to errors that smoothly degrade as the underlying problem instance changes, and we prove high quality predictions can be learned from a small sample of prior instances.

中文翻译：

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在线匹配，流量和负载平衡的易学且实例稳定的预测

本文提出了一种新的模型，该模型具有超出最坏情况下算法性能的有用预测的有用预测。通过完善现有模型，我们的模型可确保预测可正式学习且实例稳定。可学习性保证了可以根据过去的数据有效地构建预测。实例鲁棒性正式确保预测对于问题输入的适度变化是鲁棒的。此外，鲁棒性模型确保可以客观地比较两个不同的预测，从而解决了现有模型中的缺点。本文建立了满足这些性质的预测的存在。本文考虑了具有预测网络流量分配问题和受限分配制造期最小化问题的在线算法。对于这两个问题，