Summary of Private Online Learning Via Lazy Algorithms, by Hilal Asi et al.
Private Online Learning via Lazy Algorithms
by Hilal Asi, Tomer Koren, Daogao Liu, Kunal Talwar
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Data Structures and Algorithms (cs.DS); Optimization and Control (math.OC); 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 This paper tackles the challenge of private online learning, focusing on two key problems: Online Prediction from Experts (OPE) and Online Convex Optimization (OCO). The authors develop a novel transformation that converts lazy online learning algorithms into privately computable ones. By applying this transformation to existing OPE and OCO algorithms, they create differentially private variants with improved regret bounds. Specifically, the proposed DP-OPE and DP-OCO algorithms achieve regret of + T^{1/3} (d)/^{2/3} and + T^{1/3} /^{2/3}, respectively. The authors also establish a lower bound for DP-OPE, demonstrating that these rates are optimal for certain classes of private algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making online learning more private and secure. Online learning involves using expert advice or optimization methods to make predictions or solve problems over time. The challenge is to do this while protecting people’s privacy and personal data. The authors come up with a new way to transform existing algorithms into privately computable ones, which helps improve the performance of these algorithms in terms of regret (a measure of how well they do). They apply their method to two specific problems: predicting expert advice and optimizing convex functions. Their results show that their approach can achieve better regret bounds than previous methods. |
Keywords
» Artificial intelligence » Online learning » Optimization
