Summary of Sgd with Clipping Is Secretly Estimating the Median Gradient, by Fabian Schaipp et al.
SGD with Clipping is Secretly Estimating the Median Gradient
by Fabian Schaipp, Guillaume Garrigos, Umut Simsekli, Robert Gower
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); 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 explores ways to improve robustness in stochastic optimization problems, which are crucial in various applications where data is noisy or corrupted. For instance, distributed learning with faulty nodes, privacy-constrained learning, and heavy-tailed noise due to algorithm dynamics. The authors propose a median-based approach for estimating gradients, showing that it can converge even under heavy-tailed state-dependent noise. They also derive iterative methods using the stochastic proximal point method and generalize these to compute geometric medians. Furthermore, they introduce an algorithm for estimating median gradients across iterations, revealing that several well-known methods, including clipping techniques, are special cases within this framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure our computer programs work better when there’s a lot of noise or mistakes in the data. Imagine trying to learn from a bunch of noisy photos – it would be hard! This happens in many real-world applications like sharing files online or protecting people’s privacy. The researchers came up with a new way to make our algorithms more robust, using something called the median. They showed that this approach can actually work even when there are lots of errors in the data. They also developed special techniques to help us compute these medians and applied them to several different methods. |
Keywords
* Artificial intelligence * Optimization
