bandit
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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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Context-lumpable stochastic bandits
Consider a recommendation platform that interacts with a finite set of users in an online fashion. Users arrive at the platform and receive a recommendation.
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Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning
Both algorithms yield state-of-the-art performance guarantees.
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Decentralized Randomly Distributed Multi-agent Multi-armed Bandit with Heterogeneous Rewards
Each client pulls an arm and communicates with neighbors based on the graph provided by the environment.
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Efficient Algorithms for Generalized Linear Bandits with Heavy-tailed Rewards
Lijun Zhang is the corresponding author.
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