RepUCB: Representation Learning-Based UCB for Heterogeneous Multi-Task Linear Bandits
Multi-task representation learning exploits the shared structure among related tasks by learning a common latent representation, thereby improving sample efficiency. This paper introduces a novel approach to multi-task representation learning in heterogeneous linear bandits. We consider $T$ concurrent heterogeneous linear bandit tasks, each with feature dimension $d$, whose reward parameters share a common latent representation of dimension $r \ll \min\{d, T\}$, capturing the underlying task relatedness. We propose RepUCB, a novel Upper Confidence Bound (UCB) algorithm that leverages shared low-rank representations to enhance learning in a sample-efficient manner. Our algorithm first coll...
| Authors | Jiabin Lin, Shana Moothedath |
|---|---|
| Primary category | cs.LG |
| Published | 1 April 2026 |
| Last updated at source | 22 September 2026 |
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