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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