Extreme bandits
In many areas of medicine, security, and life sciences, we want to allocate limited resources to different sources in order to detect extreme values. In this paper, we study an efficient way to allocate these resources sequentially under limited feedback. While sequential design of experiments is well studied in bandit theory, the most commonly optimized property is the regret with respect to the maximum mean reward. However, in other problems such as network intrusion detection, we are interested in detecting the most extreme value output by the sources. Therefore, in our work we study extreme regret which measures the efficiency of an algorithm compared to the oracle policy selecting th...
| Authors | Alexandra Carpentier, Michal Valko |
|---|---|
| Primary category | stat.ML |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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