MAMS

Unified MAIR framework bridges GP-UCB and DEC approaches in kernel bandits

A new arXiv preprint unifies two major theoretical frameworks for frequentist RKHS bandits — Gaussian-process upper confidence bound (GP-UCB) and decision-estimation-coefficient (DEC) methods — under a common algorithmic-information language called MAIR. The paper generalizes both the GP-UCB analysis and the MAMS algorithm, proposes a safeguarded master algorithm combining their advantages, and demonstrates that algorithmic complexity can be more informative than class-wide minimax certificates in overparameterized models. The work clarifies a foundational distinction between algorithmic information and minimax coefficients in bandit theory.