Summary of Supervised Kernel Thinning, by Albert Gong et al.
Supervised Kernel Thinning
by Albert Gong, Kyuseong Choi, Raaz Dwivedi
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST); 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 presents a generalization of the kernel thinning algorithm (KT) to speed up supervised learning problems involving kernel methods. By combining KT with Nadaraya-Watson regression or kernel smoothing, and kernel ridge regression, the authors achieve a quadratic speed-up in both training and inference times. The KT-based regression estimators enjoy significantly superior computational efficiency over full-data estimators and improved statistical efficiency over i.i.d. subsampling of the training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to do machine learning faster! They took an algorithm called kernel thinning and made it work with other types of algorithms that are used for supervised learning, like regression and smoothing. This means we can train models and make predictions way quicker than before, without losing accuracy. The authors tested their idea with simulations and real data and it worked well. |
Keywords
» Artificial intelligence » Generalization » Inference » Machine learning » Regression » Supervised
