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

23 Jan 2025arXiv:2501.13390archive 2025-07-28

Thang Duong, Zhi Wang, Chicheng Zhang

We study lifelong learning in linear bandits, where a learner interacts with a sequence of linear bandit tasks whose parameters lie in an m-dimensional subspace of ℝᵈ, thereby sharing a low-rank representation. 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 multi-task linear bandits without the task diversity assumption. We develop an algorithm that efficiently learns and transfers low-rank representations. When facing N tasks, each played over τ rounds, our algorithm achieves a regret guarantee of Õ(Nm √(τ) + N^(2/3) τ^(2/3) d m^(1/3) + Nd² + τm d ) under the ellipsoid action set assumption. This result can significantly improve upon the baseline of Õ (Nd √(τ)) that does not leverage the low-rank structure when the number of tasks N is sufficiently large and m ≪d. We also demonstrate empirically on synthetic data that our algorithm outperforms baseline algorithms, which rely on the task diversity assumption.

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