Set of cryptocurrencies on dollar bills. (IMAGE)

Chung Ang University

Set of cryptocurrencies on dollar bills.

Caption

The problem of sequential decision-making for noisy rewards in data science usually assumes a light-tailed noise distribution. However, many real-world datasets actually show a heavy-tailed noise. To address this, researchers from Korea proposed a minimax optimal robust upper confidence bound (MR-UCB) method and tested its validity with cryptocurrency datasets. Their method could identify the most profitable cryptocurrency with the least time-averaged cumulative regret.

Credit

Marco Verch from flickr (https://www.flickr.com/photos/149561324@N03/32889807948)

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