Learning system agreement

Chung-Ang University Researchers Develop Algorithm for Optimal Decision Making Under Heavy-tailed Noisy Rewards

Retrieved on: 
Thursday, November 24, 2022
Learning system agreement, APE, Algorithm, Chung-Ang University, Neural network, Reward, Data science, UCB, Pareto, Assistant, Neutralizing antibody, MAB, Noise, Advertising, Mining

SEOUL, South Korea, Nov. 23, 2022 /PRNewswire/ -- In data science, researchers typically deal with data that contain noisy observations.

Key Points: 
  • SEOUL, South Korea, Nov. 23, 2022 /PRNewswire/ -- In data science, researchers typically deal with data that contain noisy observations.
  • An important problem explored by data scientists in this context is the problem of sequential decision making.
  • Here, an intelligent agent sequentially explores and selects actions based on noisy rewards under an uncertain environment.
  • Its goal is to minimize the cumulative regretthe difference between the maximum reward and the expected reward of selected actions.