Summary of How to Make the Gradients Small Privately: Improved Rates For Differentially Private Non-convex Optimization, by Andrew Lowy et al.
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization
by Andrew Lowy, Jonathan Ullman, Stephen J. Wright
First submitted to arxiv on: 17 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Optimization and Control (math.OC)
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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 a novel framework for designing differentially private algorithms to find approximate stationary points of non-convex loss functions. The proposed approach uses a private approximate risk minimizer as a “warm start” for another private algorithm, enabling improved rates for various classes of non-convex loss functions. Specifically, the authors achieve optimal or near-optimal rates for finding stationary points of smooth empirical loss functions, quasar-convex functions, and population loss functions, as well as for non-convex generalized linear models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us find answers to tricky math problems in a way that keeps our personal information private. It shows how to use computers to find the best answer among many possible solutions, while making sure that no one can figure out what any individual’s solution is. This is important because we often need to make decisions based on lots of different data, but we don’t want anyone to be able to use that data to learn secrets about us. |
