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Computer Science > Machine Learning

arXiv:2204.10979 (cs)
[Submitted on 23 Apr 2022 (v1), last revised 13 Jan 2023 (this version, v2)]

Title:Smoothed Online Combinatorial Optimization Using Imperfect Predictions

Authors:Kai Wang, Zhao Song, Georgios Theocharous, Sridhar Mahadevan
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Abstract:Smoothed online combinatorial optimization considers a learner who repeatedly chooses a combinatorial decision to minimize an unknown changing cost function with a penalty on switching decisions in consecutive rounds. We study smoothed online combinatorial optimization problems when an imperfect predictive model is available, where the model can forecast the future cost functions with uncertainty. We show that using predictions to plan for a finite time horizon leads to regret dependent on the total predictive uncertainty and an additional switching cost. This observation suggests choosing a suitable planning window to balance between uncertainty and switching cost, which leads to an online algorithm with guarantees on the upper and lower bounds of the cumulative regret. Empirically, our algorithm shows a significant improvement in cumulative regret compared to other baselines in synthetic online distributed streaming problems.
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2204.10979 [cs.LG]
  (or arXiv:2204.10979v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2204.10979

Submission history

From: Kai Wang [view email]
[v1] Sat, 23 Apr 2022 02:30:39 UTC (403 KB)
[v2] Fri, 13 Jan 2023 19:56:52 UTC (438 KB)
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