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arXiv Machine Learning By Xia Jiang, Rong J. B. Zhu

Empirical Bayesian Multi-Bandit Learning

Read the original on arXiv Machine Learning →

The paper introduces a hierarchical Bayesian framework for multi-task contextual bandits, capturing both heterogeneity and correlations among bandit instances. It proposes an empirical Bayesian method to estimate the covariance matrix of the prior, leading to two new algorithms—ebmTS and ebmUCB—that incorporate this prior into decision making. The authors provide frequentist regret bounds and show through synthetic and real-world experiments that these algorithms outperform existing methods, especially in complex settings.

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