AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work obtains an algorithm that works for a large class of ``monotone''stochastic problems, including prophet inequality, Pandora's box, series testing, stochastic knapsack and stochastic matching, and improves over previous regret bounds by a $\sqrt{n}$ factor.
Firms can improve decisions as data arrive without pausing operations for a separate data-gathering phase, and even with partial, censored, or yes-or-no feedback, the cumulative performance gap grows essentially at the square root rate.
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