Summary of Fixed-budget Differentially Private Best Arm Identification, by Zhirui Chen et al.
Fixed-Budget Differentially Private Best Arm Identification
by Zhirui Chen, P. N. Karthik, Yeow Meng Chee, Vincent Y. F. Tan
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT); Statistics Theory (math.ST); 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 investigates best arm identification (BAI) in linear bandits while ensuring differential privacy. In this setting, the goal is to minimize the error probability of finding the arm with the largest mean reward after a finite number of sampling rounds, subject to the constraint that the decision maker’s policy satisfies -differential privacy (-DP). The authors propose a policy called DP-BAI, which uses the principle of maximum absolute determinants and derives an upper bound on its error probability. They also establish a minimax lower bound on the error probability, showing that both bounds decay exponentially with the number of sampling rounds. Additionally, the paper presents auxiliary results that contribute to the derivation of the lower bound, potentially applicable to other bandit problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers explore how to find the best option (arm) in a situation where you have limited information and want to keep your choices private. They came up with a new way to make decisions called DP-BAI that keeps your choices secret while still helping you make good decisions. The authors also showed that their method is better than other ways of making decisions, even when you only have a little time left to decide. |
Keywords
* Artificial intelligence * Probability
