Summary of Robust Regression with Ensembles Communicating Over Noisy Channels, by Yuval Ben-hur and Yuval Cassuto
Robust Regression with Ensembles Communicating over Noisy Channels
by Yuval Ben-Hur, Yuval Cassuto
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Information Theory (cs.IT)
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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 A new approach addresses reliability challenges in distributed machine learning settings, where multiple devices with varying capabilities collaborate to perform a joint task over noisy communication channels. The study focuses on ensembling regression models that communicate through additive noise channels, aiming to optimize aggregation coefficients for correlated noise parameters. This work applies to leading state-of-the-art ensemble methods like bagging and gradient boosting, demonstrating effectiveness on both synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Distributed machine learning allows many devices to work together to make predictions. But when these devices are different and the communication between them is noisy, it’s hard to get accurate results. A team of researchers developed new methods to help devices with low precision or errors in their calculations communicate effectively. This helps improve the accuracy of the overall prediction. The study tested these methods on both made-up data and real-world datasets, showing they can be useful. |
Keywords
» Artificial intelligence » Bagging » Boosting » Machine learning » Precision » Regression
