Summary of Differentially Private Best-arm Identification, by Achraf Azize et al.
Differentially Private Best-Arm Identification
by Achraf Azize, Marc Jourdan, Aymen Al Marjani, Debabrota Basu
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 In this paper, researchers tackle the Best Arm Identification (BAI) problem in the context of data-sensitive applications like clinical trials and hyperparameter tuning. They focus on BAI with fixed confidence in local and global models while ensuring Differential Privacy (DP). The authors derive lower bounds on the sample complexity for any correct BAI algorithm satisfying either local or global DP, revealing two privacy regimes: high-privacy and low-privacy. In the high-privacy regime, hardness depends on a coupled effect of privacy and novel information-theoretic quantities involving Total Variation. They propose variants of Top Two algorithms, CTB-TT and AdaP-TT*, for local and global DP respectively, showcasing asymptotic optimality and good privacy-utility trade-offs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make data-sensitive applications more private. It’s about identifying the best option when there are many choices, but you only want to know one thing (like which medicine works best). The researchers want to keep your data safe, so they create new ways to do this that balance how much privacy you need with how useful the information is. |
Keywords
» Artificial intelligence » Hyperparameter
