Summary of Hyperparameter Optimization For Randomized Algorithms: a Case Study on Random Features, by Oliver R. A. Dunbar et al.
Hyperparameter Optimization for Randomized Algorithms: A Case Study on Random Features
by Oliver R. A. Dunbar, Nicholas H. Nelsen, Maya Mutic
First submitted to arxiv on: 30 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation (stat.CO); 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 In this paper, researchers introduce a novel approach to optimizing hyperparameters in randomized algorithms like Random Feature Regression (RFR), which accelerates Gaussian Process Regression. The key innovation is a random objective function tailored for hyperparameter tuning, minimizing it with Ensemble Kalman Inversion (EKI). This gradient-free optimizer is scalable and robust to randomness. The authors showcase the effectiveness of this methodology on several problems, including global sensitivity analyses, chaotic dynamics integration, and Bayesian inverse problems in atmospheric dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Randomized algorithms can be super helpful for making complex calculations faster. One way they do this is by using random features to approximate functions instead of computing them exactly. This paper shows how to optimize the settings for these randomized algorithms so that they work well on different types of problems. They use a special technique called Ensemble Kalman Inversion, which doesn’t need gradients and can handle big datasets. |
Keywords
» Artificial intelligence » Hyperparameter » Objective function » Regression
