Summary of Pure Exploration For Constrained Best Mixed Arm Identification with a Fixed Budget, by Dengwang Tang et al.
Pure Exploration for Constrained Best Mixed Arm Identification with a Fixed Budget
by Dengwang Tang, Rahul Jain, Ashutosh Nayyar, Pierluigi Nuzzo
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 The paper introduces the constrained best mixed arm identification (CBMAI) problem, where the goal is to find the optimal randomized mixture of multiple arms that maximizes expected reward while satisfying constraints on expected costs within a given learning budget. A novel algorithm called Score Function-based Successive Reject (SFSR) is proposed, combining classical successive reject framework with linear programming theory. Theoretical upper bounds are established for mis-identification probability, showing exponential decay in the budget and problem instance hardness. An information-theoretic lower bound is also developed to validate these findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a new problem called constrained best mixed arm identification (CBMAI). It’s like trying to find the best combination of options that gives you the most reward, but you have to stay within a certain budget and can’t exceed it. The researchers created a new way to solve this problem using something called linear programming theory. They also showed how well their method works by testing it on different scenarios. |
Keywords
» Artificial intelligence » Probability
