Summary of Computation-aware Gaussian Processes: Model Selection and Linear-time Inference, by Jonathan Wenger et al.
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
by Jonathan Wenger, Kaiwen Wu, Philipp Hennig, Jacob R. Gardner, Geoff Pleiss, John P. Cunningham
First submitted to arxiv on: 1 Nov 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 extends recent developments in computational uncertainty to model selection in Gaussian processes, enabling an explicit tradeoff between computation and precision. The approach scales linearly with the size of the dataset, allowing for efficient hyperparameter optimization using a novel training loss. Experimental results demonstrate that this method outperforms state-of-the-art methods on medium to large-scale datasets, including SGPR, CGGP, and SVGP. This breakthrough enables Gaussian processes to be trained on large-scale datasets without compromising their ability to quantify uncertainty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Gaussian processes are a powerful tool for machine learning, but they can get slow when dealing with really big datasets. Researchers have been working on ways to make them faster while still keeping the results accurate. A new method has been developed that makes it possible to select the best model from many options without having to sacrifice too much speed or accuracy. This is important because selecting the right model is crucial for making good decisions, especially when there’s a lot of uncertainty involved. |
Keywords
* Artificial intelligence * Hyperparameter * Machine learning * Optimization * Precision
