Summary of Improved Sample Complexity For Private Nonsmooth Nonconvex Optimization, by Guy Kornowski et al.
Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization
by Guy Kornowski, Daogao Liu, Kunal Talwar
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); 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 presents new differentially private optimization algorithms for solving stochastic and empirical objectives. The proposed methods return a Goldstein-stationary point with improved sample complexity bounds compared to existing works. A single-pass algorithm is designed that returns an (α,β)-stationary point as long as the dataset size meets certain conditions. This algorithm has a smaller sample complexity than previous work by Zhang et al. [2024]. The paper also presents a multi-pass polynomial time algorithm that further improves the sample complexity using a sample-efficient ERM algorithm and proving that Goldstein-stationary points generalize to the population loss. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers learn new things without sharing personal information. It develops special algorithms that can do this efficiently, even when the data isn’t in a specific order or shape. The goal is to find a good “stationary point” that works well for both small and large datasets. The results are promising, showing that these algorithms can be much faster than previous methods. |
Keywords
* Artificial intelligence * Optimization
