Summary of Confidence Interval Construction and Conditional Variance Estimation with Dense Relu Networks, by Carlos Misael Madrid Padilla et al.
Confidence Interval Construction and Conditional Variance Estimation with Dense ReLU Networks
by Carlos Misael Madrid Padilla, Oscar Hernan Madrid Padilla, Yik Lun Kei, Zhi Zhang, Yanzhen Chen
First submitted to arxiv on: 29 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 In this paper, researchers tackle the challenges of estimating conditional variance and constructing confidence intervals in nonparametric regression using dense networks with ReLU activation functions. They present a residual-based framework for conditional variance estimation, deriving bounds for both heteroscedastic and homoscedastic settings. By relaxing sub-Gaussian noise assumptions, the proposed bounds accommodate sub-Exponential noise and beyond. The authors also develop non-asymptotic bounds for conditional mean and variance estimation in ReLU network estimators, representing a significant advancement in uncertainty quantification and confidence interval construction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve problems with estimating how much data varies and building confidence intervals in deep learning. It creates a new way to do this using special kinds of computer networks called dense networks with ReLU activation functions. The researchers show how to use these networks to get good estimates of both the average value (mean) and how spread out the data is (variance). They also create a new way to build confidence intervals for the true mean that guarantees the right level of coverage, which is important for deep learning applications. |
Keywords
» Artificial intelligence » Deep learning » Regression » Relu
