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Summary of Satisficing Regret Minimization in Bandits, by Qing Feng et al.


Satisficing Regret Minimization in Bandits

by Qing Feng, Tianyi Ma, Ruihao Zhu

First submitted to arxiv on: 10 Jun 2024

Categories

  • Main: Machine Learning (stat.ML)
  • Secondary: Machine Learning (cs.LG)

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This paper proposes an algorithm called SELECT for satisficing exploration in bandit optimization, which aims to select arms with mean reward exceeding a certain threshold value as frequently as possible. The algorithm achieves constant satisficing regret for various bandit optimization problems in the realizable case, where a satisficing arm exists. In the non-realizable case, SELECT enjoys the same standard regret guarantee as the oracle. The paper also presents numerical experiments validating the performance of SELECT for popular bandit optimization settings.
Low GrooveSquid.com (original content) Low Difficulty Summary
This research develops an algorithm to find arms with high rewards in bandit optimization problems. Imagine you’re playing a game where you have to choose between different options, and some options are better than others. The goal is to pick the best option as much as possible. This algorithm, called SELECT, helps you do that by trying out different options and seeing which one gives you the most reward.

Keywords

» Artificial intelligence  » Optimization