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Adaptivity in Stochastic Submodular Cover Solutions to stochastic optimization problems are typically sequential decision processes that make decisions one by one, waiting for (and using) the feedback from each decision. Whereas such “adaptive” solutions achieve the best objective, they can be very time-consuming because of the need to wait for feedback after each decision. A natural question is are there solutions that only adapt (i.e., wait for feedback) a few times whereas still being competitive with the fully adaptive optimal solution? In “The Power of Adaptivity for Stochastic Submodular Cover,” Ghuge, Gupta, and Nagarajan resolve this question in the context of stochastic submodular cover, which is a fundamental stochastic covering problem. They provide algorithms that achieve a smooth trade-off between the number of adaptive “rounds” and the solution quality. The authors also demonstrate via experiments on real-world and synthetic data sets that, even for problems with more than 1,000 decisions, about six rounds of adaptivity suffice to obtain solutions nearly as good as fully adaptive solutions.