Summary of Scaling Gaussian Processes For Learning Curve Prediction Via Latent Kronecker Structure, by Jihao Andreas Lin et al.
Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure
by Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, Eytan Bakshy
First submitted to arxiv on: 11 Oct 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 The proposed method models learning curves of machine learning models as a function of model hyper-parameters and training progression, a crucial task in AutoML. The approach leverages Gaussian processes (GPs) with latent Kronecker structure to efficiently handle missing values and early-stopping. This allows for reduced computational complexity, with the method requiring only O(n^3 + m^3) time and O(n^2 + m^2) space. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research develops a new way to model how machine learning models improve over time. It uses a type of Gaussian process called a GP to make predictions about what will happen next, given some information about the past. The method is efficient and can handle missing data, which makes it useful for real-world applications. |
Keywords
» Artificial intelligence » Early stopping » Machine learning
