Summary of Learning Deep Kernels For Non-parametric Independence Testing, by Nathaniel Xu and Feng Liu and Danica J. Sutherland
Learning Deep Kernels for Non-Parametric Independence Testing
by Nathaniel Xu, Feng Liu, Danica J. Sutherland
First submitted to arxiv on: 10 Sep 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 The proposed scheme for selecting kernels used in Hilbert-Schmidt Independence Criterion (HSIC)-based independence tests leverages maximizing an estimate of asymptotic test power to identify reasonable kernel choices. The approach addresses limitations of commonly-used kernels, such as Gaussian and distance covariance, which may not be effective for smaller sample sizes or complex data distributions. By approximately maximizing true test power, the learned kernels can detect structured dependence between random variables in various experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research proposes a new way to choose the right tools (kernels) for testing if two sets of data are connected or independent. Currently, these tools have limitations and may not work well with smaller datasets or complex connections between the data. The new approach helps find better kernels that can identify patterns in the data, which is important for understanding how different things relate to each other. |
