Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints

arXiv:2401.11563v2 Announce Type: replace
Abstract: We present the problem of conservative distributed multi-task learning in stochastic linear contextual bandits with heterogeneous agents. This extends conservative linear bandits to a distributed setting where M agents tackle different but related tasks while adhering to stage-wise performance constraints. The exact context is unknown, and only a context distribution is available to the agents as in many practical applications that involve a prediction mechanism to infer context, such as stock market prediction and weather …

abstract agents arxiv constraints context cs.lg cs.ma distributed distribution linear multi-task learning performance stage stochastic tasks type wise