Multi-Task Learning for Contextual Bandits

TL;DR

This paper introduces a multi-task learning framework for contextual bandits that leverages similarities across different arms to improve reward prediction, providing theoretical regret bounds and practical algorithms for estimating task similarity.

Contribution

It proposes an upper confidence bound-based multi-task learning algorithm for contextual bandits, with theoretical regret analysis and a method for estimating task similarity from data.

Findings

The algorithm outperforms baseline methods on several datasets.

Theoretical regret bounds demonstrate the benefits of leveraging task similarity.

Effective estimation of task similarity enhances learning performance.

Abstract

Contextual bandits are a form of multi-armed bandit in which the agent has access to predictive side information (known as the context) for each arm at each time step, and have been used to model personalized news recommendation, ad placement, and other applications. In this work, we propose a multi-task learning framework for contextual bandit problems. Like multi-task learning in the batch setting, the goal is to leverage similarities in contexts for different arms so as to improve the agent's ability to predict rewards from contexts. We propose an upper confidence bound-based multi-task learning algorithm for contextual bandits, establish a corresponding regret bound, and interpret this bound to quantify the advantages of learning in the presence of high task (arm) similarity. We also describe an effective scheme for estimating task similarity from data, and demonstrate our algorithm's performance on several data sets.