A Practical Algorithm for Feature-Rich, Non-Stationary Bandit Problems

ArXi:2603.16755v1 Announce Type: new Contextual bandits are incredibly useful in many practical problems. We go one step further by devising a realistic problem that combines: (1) contextual bandits with dense arm features, (2) non-linear reward functions, and (3) a generalization of correlated bandits where reward distributions change over time but the degree of correlation maintains. This formulation lends itself to a wider set of applications such as recommendation tasks. To solve this problem, we.