AI RESEARCH
Multi-Task Representation Learning for Conservative Linear Bandits
arXiv CS.LG
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ArXi:2605.12176v1 Announce Type: new This paper presents the Constrained Multi-Task Representation Learning (CMTRL) framework for linear bandits. We consider T linear bandit tasks in a d dimensional space, which share a common low-dimensional representation of dimension r, where r is much smaller than the minimum of d and T. Furthermore, tasks are constrained so that only actions meeting specific safety or performance requirements are allowed, referred to as conservative (safe) bandits. We