Online decision-makers 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 accuracy is unknown to decision-makers a priori, hence blindly following the predictions can be harmful. In this paper, we address this problem by developing algorithms that utilize predictions in a manner that is robust to the unknown prediction accuracy.
We consider the Online Resource Allocation Problem, a generic model for online decision-making, in which a limited amount of resources may be used to satisfy a sequence of arriving requests. Prior work has characterized the best achievable performances when the arrivals are either generated stochastically (i.i.d.) or completely adversarially, and shown that algorithms exist which match these bounds under both arrival models, without ``knowing'' the underlying model. To this backdrop, we introduce predictions in the form of shadow prices on each type of resource. Prediction accuracy is naturally defined to be the distance between the predictions and the actual shadow prices.
We tightly characterize, via a formal lower bound, the extent to which any algorithm can optimally leverage predictions (that is, to ``follow'' the predictions when accurate, and ``ignore'' them when inaccurate) without knowing the prediction accuracy or the underlying arrival model. Our main contribution is then an algorithm which achieves this lower bound. Finally, we empirically validate our algorithm with a large-scale experiment on real data from the retailer H&M.
Collective Departure Time Allocation in Large-scale Urban Networks: A Flexible Modeling Framework with Trip Length and Desired Arrival Time Distributions
Online Learning and Matching for Resource Allocation Problems
University of California, Irvine（加州大学欧文分校）；DAMO Academy, Alibaba US（阿里巴巴美国达摩院）；Amherst College（阿默斯特学院）；University of California, Los Angeles（加州大学洛杉矶分校）；University College London（伦敦大学学院）；Purdue University（普渡大学）
Dynamic Core Allocation for Malleable Jobs with Unknown Speed-up Parameters
Korteweg-de Vries Institute for Mathematics, University of Amsterdam（阿姆斯特丹大学科特维格-德弗里斯数学研究所）；Mathematical Institute, Leiden University（莱顿大学数学研究所）；Eurandom, Eindhoven University of Technology（埃因霍温理工大学尤拉当）；Department of Statistics, University of Haifa（海法大学统计学系）
Modern Primal-Dual Frameworks for Prior-Free Online Resource Allocation
University of Chicago Booth School of Business（芝加哥大学布斯商学院）；Department of Industrial Engineering & Operations Research, University of California, Berkeley（加州大学伯克利分校工业工程与运筹学系）
