Co-Exploration and Co-Exploitation via Shared Structure in Multi-Task Bandits
This addresses the challenge of balancing exploration and exploitation in multi-task bandit problems for applications like personalized recommendations, though it appears incremental as an extension of Bayesian methods to this specific setting.
The paper tackles the problem of efficient exploration in contextual multi-task multi-armed bandits with partially observed contexts and latent dependencies, proposing a Bayesian framework that learns a global joint distribution while allowing personalized inference. The result shows the method outperforms hierarchical model baselines, particularly in settings with model misspecification or complex latent heterogeneity.
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.