Nonstationary Stochastic Bandits: UCB Policies and Minimax Regret

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Cite
Wei, Lai, and Vaibhav Srivastava. “Nonstationary Stochastic Bandits: UCB Policies and Minimax Regret”. IEEE Open Journal of Control Systems, vol. 3, 2024, pp. 128-42, https://doi.org/10.1109/ojcsys.2024.3372929.
Wei, L., & Srivastava, V. (2024). Nonstationary Stochastic Bandits: UCB Policies and Minimax Regret. IEEE Open Journal of Control Systems, 3, 128-142. https://doi.org/10.1109/ojcsys.2024.3372929
Wei L, Srivastava V. Nonstationary Stochastic Bandits: UCB Policies and Minimax Regret. IEEE Open Journal of Control Systems. 2024;3:128-42.
Journal Categories
Technology
Mechanical engineering and machinery
Technology
Mechanical engineering and machinery
Control engineering systems
Automatic machinery (General)
Refrences
Title Journal Journal Categories Citations Publication Date
The sample complexity of exploration in the multi-armed bandit problem 2004
A simple approach for non-stationary linear bandits 2020
Nearly optimal adaptive procedure with change detection for piecewise-stationary bandit 2019
A new algorithm for non-stationary contextual bandits: Efficient, optimal, and parameter-free 2019
Adaptively tracking the best bandit arm with an unknown number of distribution changes 2019