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Multi-Agent Learning with Heterogeneous Linear Contextual Bandits
UCB, wherein agents cooperatively minimize the group regret under the coordination of a central server.
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Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator
Karl Krauth, Stephen Tu, Benjamin Recht
Neural Information Processing Systems http://nips.cc/
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Minimax Regret for Cascading Bandits
Cascading bandits is a natural and popular model that frames the task of learning to rank from Bernoulli click feedback in a bandit setting.
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Linear Stochastic Bandits Under Safety Constraints
Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis
Neural Information Processing Systems http://nips.cc/
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- North America > United States > California > Santa Barbara County > Santa Barbara (0.04)
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