Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms

TL;DR

This paper introduces Syndicated Bandits, a novel framework for automatically tuning multiple hyper-parameters in contextual bandit algorithms in real-time, avoiding exponential regret growth and achieving optimal performance.

Contribution

It proposes a general Syndicated Bandits framework for dynamic hyper-parameter tuning in contextual bandits, with proven regret bounds and broad applicability.

Findings

Regret bounds are derived for the framework.

The method avoids exponential regret dependence on hyper-parameters.

Experimental results validate effectiveness on synthetic and real data.

Abstract

The stochastic contextual bandit problem, which models the trade-off between exploration and exploitation, has many real applications, including recommender systems, online advertising and clinical trials. As many other machine learning algorithms, contextual bandit algorithms often have one or more hyper-parameters. As an example, in most optimal stochastic contextual bandit algorithms, there is an unknown exploration parameter which controls the trade-off between exploration and exploitation. A proper choice of the hyper-parameters is essential for contextual bandit algorithms to perform well. However, it is infeasible to use offline tuning methods to select hyper-parameters in contextual bandit environment since there is no pre-collected dataset and the decisions have to be made in real time. To tackle this problem, we first propose a two-layer bandit structure for auto tuning the exploration parameter and further generalize it to the Syndicated Bandits framework which can learn multiple hyper-parameters dynamically in contextual bandit environment. We derive the regret bounds of our proposed Syndicated Bandits framework and show it can avoid its regret dependent exponentially in the number of hyper-parameters to be tuned. Moreover, it achieves optimal regret bounds under certain scenarios. Syndicated Bandits framework is general enough to handle the tuning tasks in many popular contextual bandit algorithms, such as LinUCB, LinTS, UCB-GLM, etc. Experiments on both synthetic and real datasets validate the effectiveness of our proposed framework.