Rate-adaptive model selection over a collection of black-box contextual bandit algorithms

This project is a theoretical research artifact associated with the 2020 paper 'Adaptivity and Model Selection for Contextual Bandits'. From a competitive intelligence perspective, it has near-zero defensibility as a software product: with 0 stars and no activity for years (2135 days), it lacks any community, maintenance, or developer mindshare. The value lies entirely in the underlying mathematical proof and the algorithm logic, which can be easily re-implemented in production-grade frameworks like Vowpal Wabbit or Ray RLLib. While the theoretical contribution—providing rate-adaptive selection over black-boxes—was significant at the time of publication, the bandit field moves quickly. More recent research (e.g., by authors like Pacchiano or Foster himself) has likely refined these bounds. Frontier labs are unlikely to care about this as a standalone tool, as they focus on massive-scale RLHF, but the 'master algorithm' pattern is a standard technique that will eventually be consolidated into enterprise AutoML and recommendation engine platforms. It is more of an academic benchmark than a defensible project.