Summary of Nonparametric Density Estimation Via Variance-reduced Sketching, by Yifan Peng et al.
Nonparametric Density Estimation via Variance-Reduced Sketching
by Yifan Peng, Yuehaw Khoo, Daren Wang
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Numerical Analysis (math.NA); Methodology (stat.ME)
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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 This paper introduces Variance-Reduced Sketching (VRS), a new framework for estimating multivariable density functions that addresses the curse of dimensionality. Classical kernel methods become inadequate in high-dimensional settings, while neural network estimators can be unreliable. VRS conceptualizes multivariable functions as infinite-size matrices and uses sketching techniques to reduce variance. Simulated experiments and real-world data applications demonstrate its robust performance, outperforming existing methods in numerous density models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to estimate things like how likely it is that something will happen or where things tend to be found. Right now, we have ways of doing this that are good for small amounts of information, but they get bad when there’s too much. The authors came up with a new approach called Variance-Reduced Sketching (VRS) that helps fix this problem. They tested it and showed that it works better than other methods in many cases. |
Keywords
* Artificial intelligence * Neural network
