We study a general form of Smoothed Online Convex Optimization, a.k.a.

We study the problem of augmenting online algorithms with machine learned (ML) advice. In particular, we consider the \emph{multi-shop ski rental} (MSSR) problem, which is a generalization of the classical ski rental problem. In MSSR, each shop has different prices for buying and renting a pair of skis, and a skier has to make decisions on when and where to buy. We obtain both deterministic and randomized online algorithms with provably improved performance when either a single or multiple ML predictions are used to make decisions. These online algorithms have no knowledge about the quality or the prediction error type of the ML prediction. The performance of these online algorithms are robust to the poor performance of the predictors, but improve with better predictions. Extensive experiments using both synthetic and real world data traces verify our theoretical observations and show better performance against algorithms that purely rely on online decision making.

We study the problem of online decision-making with predictions.

A recent trend in online algorithms has seen the use of future predictions generated by ML techniques to bypass pessimistic worst-case lower bounds.

We illustrate the online optimization process of RCL in Figure 1. We set b = 10 and A = I for the cost function in Eqn. The testing process is almost instant and takes less than 1 second. It does not use robustification during online optimization. By Theorem 4.1, there is a trade-off (governed by ML predictions for those problem instances that are adversarial to ROBD.

We study a challenging form of Smoothed Online Convex Optimization, a.k.a.

Two paradigms have been widely studied to deal with such uncertainty.

The study of online algorithms with ML predictions has gained significant traction in the ML and algorithms research communities in recent years.