Adversarial Bandit Optimization with Globally Bounded Perturbations to Convex Losses
We study adversarial bandit optimization in which the loss functions may be non-convex and non-smooth. In each round, the learner selects an action and observes only the loss incurred at that action. The loss consists of an underlying convex and $β$-smooth component and an adversarial perturbation that may be chosen after observing the learner's action. The perturbations are subject to a global budget controlling their cumulative magnitude over time. This framework extends the globally budgeted, post-action perturbation model from underlying linear losses to general convex and $β$-smooth losses. For this broader class, we establish expected regret guarantees that explicitly characterize t...
| Authors | Zhuoyu Cheng, Kohei Hatano, Eiji Takimoto |
|---|---|
| Primary category | cs.LG |
| Published | 18 June 2026 |
| Last updated at source | 22 June 2026 |
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