Co-Exploration and Co-Exploitation via Shared Structure in Multi-Task Bandits

TL;DR

This paper introduces a Bayesian framework for multi-task bandits that leverages shared structure to improve exploration, especially under partial observations and complex latent dependencies.

Contribution

It presents a novel particle-based Bayesian approach that models joint task-reward distributions, enabling flexible discovery of inter-task and inter-arm dependencies.

Findings

Outperforms hierarchical bandit models in complex settings

Effectively handles model misspecification

Learns latent reward structures without prior assumptions

Abstract

We propose a novel Bayesian framework for efficient exploration in contextual multi-task multi-armed bandit settings, where the context is only observed partially and dependencies between reward distributions are induced by latent context variables. In order to exploit these structural dependencies, our approach integrates observations across all tasks and learns a global joint distribution, while still allowing personalised inference for new tasks. In this regard, we identify two key sources of epistemic uncertainty, namely structural uncertainty in the latent reward dependencies across arms and tasks, and user-specific uncertainty due to incomplete context and limited interaction history. To put our method into practice, we represent the joint distribution over tasks and rewards using a particle-based approximation of a log-density Gaussian process. This representation enables flexible, data-driven discovery of both inter-arm and inter-task dependencies without prior assumptions on the latent variables. Empirically, we demonstrate that our method outperforms baselines such as hierarchical model bandits, especially in settings with model misspecification or complex latent heterogeneity.