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Best of Many in Both Worlds: Online Resource Allocation with Predictions under Unknown Arrival Model
Feb. 22, 2024, 5:42 a.m. | Lin An, Andrew A. Li, Benjamin Moseley, Gabriel Visotsky
cs.LG updates on arXiv.org arxiv.org
Abstract: Online decision-makers today can often obtain predictions on future variables, such as arrivals, demands, inventories, and so on. These predictions can be generated from simple forecasting algorithms for univariate time-series, all the way to state-of-the-art machine learning models that leverage multiple time-series and additional feature information. However, the prediction quality is often unknown to decisions-makers a priori, hence blindly following the predictions can be harmful. In this paper, we address this problem by giving algorithms …
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