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.

研究了具有预测模型的平滑在线组合优化问题，提出一种在线算法在规划窗口和开关代价之间实现平衡，通过对合成在线分布流问题进行实证，证明在累积规避中取得了显着的改进。