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September 29, 2025Open Access

Adversarial Bandit over Bandits: Hierarchical Bandits for Online Configuration Management

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The hierarchical bandit approach reduces regret in parameter optimization, suggesting improved performance over traditional methods.

ABoB guarantees a worst-case regret bound of O(k^1/2 * T^1/2), comparable to traditional methods.
Under favorable conditions, regret can improve to O(k^1/4 * T^1/2), indicating better efficiency.
Simulations show ABoB achieves lower regret and faster convergence than flat methods, with up to 50% improvement.
The algorithm effectively clusters similar configurations to adapt to changing environments and optimize choices.

Avin et al. (2025) studied this question.

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