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The stochastic contextual bandit problem, which models the trade-off betweenexploration and exploitation, has many real applications, including recommendersystems, online advertising and clinical trials. As many other machine learningalgorithms, contextual bandit algorithms often have one or morehyper-parameters. As an example, in most optimal stochastic contextual banditalgorithms, there is an unknown exploration parameter which controls thetrade-off between exploration and exploitation. A proper choice of thehyper-parameters is essential for contextual bandit algorithms to perform well.However, it is infeasible to use offline tuning methods to selecthyper-parameters in contextual bandit environment since there is nopre-collected dataset and the decisions have to be made in real time. To tacklethis problem, we first propose a two-layer bandit structure for auto tuning theexploration parameter and further generalize it to the Syndicated Banditsframework which can learn multiple hyper-parameters dynamically in contextualbandit environment. We derive the regret bounds of our proposed SyndicatedBandits framework and show it can avoid its regret dependent exponentially inthe number of hyper-parameters to be tuned. Moreover, it achieves optimalregret bounds under certain scenarios. Syndicated Bandits framework is generalenough to handle the tuning tasks in many popular contextual bandit algorithms,such as LinUCB, LinTS, UCB-GLM, etc. Experiments on both synthetic and realdatasets validate the effectiveness of our proposed framework.

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