Beyond Task Diversity: Provable Representation Transfer for Sequential Multi-Task Linear Bandits

Current literature typically assumes that the tasks are diverse, i.e., their parameters uniformly span the m-dimensional subspace. This assumption allows the low-rank representation to be learned before all tasks are revealed, which can be unrealistic in real-world applications. In this work, we present the first nontrivial result for sequential multitask linear bandits without the task diversity assumption. We develop an algorithm that efficiently learns and transfers low-rank representations.