A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity

arXiv:2307.15154v2 Announce Type: replace
Abstract: We investigate the fixed-budget best-arm identification (BAI) problem for linear bandits in a potentially non-stationary environment. Given a finite arm set $\mathcal{X}\subset\mathbb{R}^d$, a fixed budget $T$, and an unpredictable sequence of parameters $\left\lbrace\theta_t\right\rbrace_{t=1}^{T}$, an algorithm will aim to correctly identify the best arm $x^* := \arg\max_{x\in\mathcal{X}}x^\top\sum_{t=1}^{T}\theta_t$ with probability as high as possible. Prior work has addressed the stationary setting where $\theta_t = \theta_1$ for all $t$ and demonstrated that the error probability decreases as $\exp(-T …

abstract a/b testing aim algorithm arm arxiv b testing budget cs.lg environment identification identify linear parameters robustness set stat.ml testing type will