Stochastic Low-Rank Tensor Bandits for Multi-Dimensional Online Decision Making
Stochastic Low-Rank Tensor Bandits for Multi-Dimensional Online Decision Making is a scholarly work, published in 2024 in ''Journal of the American Statistical Association''. The main subjects of the publication include demand response, rank, tensor, mathematical optimization, multi-armed bandit, tensor decomposition, and computer science. The authors propose two learning algorithms tensor elimination and tensor epoch-greedy for tensor bandits without context, and derive finite-time regret bounds for them.