AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work develops a batched version of vanilla policy optimization using only $O(\log T)$ regression calls and policy switches, and shows how private regression oracles yield differentially private contextual bandit algorithms without composition across batches.
This work proposes that the objective for learning simulators should be strategic robustness rather than predictive accuracy, and formulate this as a zero-sum minimax game between a model player and an adversarial policy player, which yields a provably convergent active data selection algorithm.
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