Constrained Contextual Bandits with Adversarial Contexts
We study constrained contextual bandits with adversarial contexts, where each action yields a random reward and incurs a random cost. The conditional mean reward and cost belong to known function classes and are learned through online regression oracles. The benchmark is a stationary randomized policy whose expected cost is non-positive at every observed context. We extend the seminal $\mathsf{SquareCB}$ algorithm of Foster and Rakhlin (2020), which reduces the contextual bandits to a regression problem, to the constrained setting. A surrogate reward function is constructed which penalizes the positive part of the expected cost of an action distribution, and an auxiliary variable accumula...
| Authors | Dhruv Sarkar, Abhishek Sinha |
|---|---|
| Primary category | cs.LG |
| Published | 7 May 2026 |
| Last updated at source | 13 October 2026 |
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