Summary of Learning with Shared Representations: Statistical Rates and Efficient Algorithms, by Xiaochun Niu et al.
Learning with Shared Representations: Statistical Rates and Efficient Algorithms
by Xiaochun Niu, Lili Su, Jiaming Xu, Pengkun Yang
First submitted to arxiv on: 7 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 investigates collaborative learning through latent shared feature representations, where heterogeneous clients train personalized models with improved performance and reduced sample complexity. The authors establish new upper and lower bounds on the statistical error rates for learning low-dimensional linear representations shared across clients, accounting for heterogeneity in local dataset sizes and covariate shifts. They also extend their results to nonlinear models, including logistic regression and one-hidden-layer ReLU neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn together better by sharing important features. It shows how different devices or systems can work together to create personalized models that perform well, even with limited data. The authors figure out new limits for how much error there is when machines share simple linear representations, and they also apply this to more complex models. |
Keywords
» Artificial intelligence » Logistic regression » Relu
