AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Follow the Perturbed Leader for Linear Bandits: $\sqrt{T}$ Regret over General Convex Sets
This work gives the first adversarial linear bandit algorithm based on Follow-the-Perturbed-Leader (FTPL) that achieves $\sqrt{T}$ regret over general convex action sets and develops a systematic way to analyze the stability term in the FTPL regret through the Fisher information of the perturbation distribution.