Chung-Ang University researchers develop an algorithm for optimal decision-making with heavy-tailed noisy rewards – insideBIGDATA
Researchers propose methods that theoretically guarantee minimal loss for worst-case scenarios with minimal prior information for heavy-tailed reward distributions Exploration algorithms for stochastic multi-armed bandits (MAB) – sequential decision-making problems in uncertain environments – generally assume light-tailed distributions for reward noises. However, real-world datasets often display heavy-tailed noise. In light of this, Korean researchers propose …