Summary of Recommendations For Baselines and Benchmarking Approximate Gaussian Processes, by Sebastian W. Ober et al.
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes
by Sebastian W. Ober, Artem Artemev, Marcel Wagenländer, Rudolfs Grobins, Mark van der Wilk
First submitted to arxiv on: 15 Feb 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 Gaussian processes (GPs) are a fundamental component of machine learning, offering automatic hyperparameter selection for seamless training. However, approximations are often necessary in practical scenarios, which typically require tuning. This dichotomy has led to confusion regarding the best approach for a given situation. Our research addresses this issue by providing guidelines for comparing GP approximations based on specific user expectations. Additionally, we develop a novel training procedure for the variational method introduced by Titsias [2009], eliminating the need for user input and establishing it as a strong baseline that meets our standards. By benchmarking according to these recommendations, we gain a clearer understanding of the current state-of-the-art in GP approximations and uncover ongoing challenges that future research should tackle. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Gaussian processes are an important part of machine learning that makes it easy to train models without needing to adjust many settings. But sometimes, when we’re working with real data, we need to make some compromises by using simpler versions of these processes. This can be tricky because it’s hard to know which approach is best for a particular situation. Our research helps solve this problem by giving guidelines on how to compare the different versions of Gaussian processes and find the one that works best. We also show how to train a special type of GP model in a way that doesn’t require any user input, making it a strong starting point for further research. |
Keywords
* Artificial intelligence * Hyperparameter * Machine learning
