itself is computationally intractable in general. At this moment, designing a provably optimal contextual bandit algorithm that is applicable for large-scale real-world deployments is still widely deemed a very challenging task (see @agarwal2016making [@foster2020beyond]).

Recently, [@foster2018practical] propose an approach to solve contextual bandits with general model representations (i.e., general function classes) using an *offline regression oracle* — an oracle that can typically be implemented efficiently and has wide availability for numerous function classes due to its core role in modern machine learning. In particular, the (weighted) least-squares regression oracle assumed in the algorithm of [@foster2018practical] is highly practical as it has a strongly convex loss function and is amenable to gradient-based methods. As [@foster2018practical] point out, designing offline-regression-oracle-based algorithms is a promising direction for making contextual bandits practical, as they seem to combine the advantages of both realizability-based and agnostic algorithms: they are general and flexible enough to work with any given function class, while using a more realistic and reasonable oracle than the computationally-expensive classification oracle. Indeed, according to multiple experiments and extensive empirical evaluations conducted by [@bietti2018contextual] and [@foster2018practical], the algorithm of [@foster2018practical] “works the best overall” compared with almost all existing approaches.

Despite its empirical success, the 