AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proves a regret bound of order $\tilde{O}(\sqrt T)$, significantly improving over the previous $\tilde{O}(T^{2/3})$ guarantee, and is the first $\tilde{O}(\sqrt T)$ regret guarantee for latent linear-dynamics bandits with bilinear reward observations and an open-loop action-sequence benchmark.
A UCB-based algorithm that constructs assortments item-wise by maximizing optimistic marginal gains, avoids black-box optimization oracles, and offers a statistically and computationally efficient solution for diversity-aware assortment optimization under uncertainty is proposed.
This work proposes an ensemble sampling algorithm that refreshes the ensemble only when the regularized Gram matrix changes substantially and achieves the sharper regret bound, and is the first ensemble-sampling guarantee that simultaneously recovers both canonical regret scalings known for randomized linear bandit alg...
This analysis provides a unified treatment of memory-induced non-stationarity and nonlinear link functions, while ensuring that the leading regret term is independent of the curvature of the link function.
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