Summary of Black-box Uniform Stability For Non-euclidean Empirical Risk Minimization, by Simon Vary et al.
Black-Box Uniform Stability for Non-Euclidean Empirical Risk Minimization
by Simon Vary, David Martínez-Rubio, Patrick Rebeschini
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The proposed algorithm offers a uniformly stable learning solution for empirical risk minimization (ERM) problems with convex and smooth losses under p-norms, where p is greater than or equal to 1. The method employs properties of uniformly convex regularizers, turning an optimization algorithm into a uniformly stable learning algorithm with optimal statistical risk bounds on excess risk. This result achieves a black-box reduction for uniform stability, which was previously posed as an open question by Attia and Koren (2022) in the Euclidean case (p=2). The proposed solution has applications in addressing binary classification problems that leverage non-Euclidean geometry. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new algorithm helps computers learn from mistakes and improve their performance. It can work with different types of data, not just the usual ones we’re used to. This is important because it lets us use this algorithm for a wider range of tasks. The researchers solved an open problem that was previously tricky to solve. They showed that their algorithm works well even when the data doesn’t follow the typical rules. This has potential applications in areas like identifying things correctly, such as whether something is positive or negative. |
Keywords
» Artificial intelligence » Classification » Optimization
