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Chung-Ang University researchers develop algorithm for optimal decision making under heavy-tailed noisy rewards

Chung-Ang University researchers propose a new algorithm, MR-UCB and MR-APE, to tackle stochastic multi-armed bandit problems with heavy-tailed noise distributions. The methods guarantee minimal loss for worst-case scenarios with minimal prior information.

SourceChung Ang University·JournalIEEE Transactions on Neural Networks and Learning Systems·TypeComputational simulation/modeling·DateNov 22, 2022