Summary of Learning From Summarized Data: Gaussian Process Regression with Sample Quasi-likelihood, by Yuta Shikuri
Learning from Summarized Data: Gaussian Process Regression with Sample Quasi-Likelihood
by Yuta Shikuri
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 A new approach in Gaussian process regression tackles the challenge of learning from summarized data, which often arises due to confidentiality concerns or management costs associated with large-scale spatial datasets. The proposed method, sample quasi-likelihood, allows for efficient inference and learning using only representative features, summary statistics, and data point counts. This is achieved by analyzing approximation errors in marginal likelihood and posterior distribution, and specifying a variance function that characterizes the sample quasi-likelihood function. Theoretical and experimental results demonstrate that the method’s performance depends on the granularity of summarized data relative to the length scale of covariance functions. Applications in spatial modeling are highlighted through experiments on a real-world dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Gaussian process regression is a special kind of math tool used for predicting things based on patterns. Researchers have been working on making this tool better, especially when it’s hard to get all the data because it might be too big or sensitive. They wanted to figure out how to use just some basic information about the data, like averages and counts, to make predictions. The solution they came up with is called sample quasi-likelihood. It helps computers learn from this limited information by understanding where the patterns are in the data. The team tested their method on real data and showed that it works well for predicting things in a specific area. |
Keywords
» Artificial intelligence » Inference » Likelihood » Regression
