# Confidence Interval Construction and Conditional Variance Estimation with Dense ReLU Networks

arXiv: https://arxiv.org/abs/2412.20355 · HTML: https://mlsanity.com/paper/2412.20355/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 66 references that are in the map.
- First posted on arXiv 29 Dec 2024.

## Builds on (most cited first, 2 of 2)

- [On the rate of convergence of fully connected very deep neural network regression estimates](https://mlsanity.com/paper/1908.11133/) (2019)
- [Dense ReLU Neural Networks for Temporal-spatial Model](https://mlsanity.com/paper/2411.09961/) (2024)

## Built on by (most cited first, 3 of 3)

- [Risk Bounds For Distributional Regression](https://mlsanity.com/paper/2505.09075/) (2025)
- [Calibration Prediction Interval for Non-parametric Regression and Neural Networks](https://mlsanity.com/paper/2509.02735/) (2025)
- [Transfer Learning in Nonparametric Regression with Deep ReLU Networks](https://mlsanity.com/paper/2608.20255/) (2026)

## Abstract (arXiv)

This paper addresses the problems of conditional variance estimation and confidence interval construction in nonparametric regression using dense networks with the Rectified Linear Unit (ReLU) activation function. We present a residual-based framework for conditional variance estimation, deriving nonasymptotic bounds for variance estimation under both heteroscedastic and homoscedastic settings. We relax the sub-Gaussian noise assumption, allowing the proposed bounds to accommodate sub-Exponential noise and beyond. Building on this, for a ReLU neural network estimator, we derive non-asymptotic bounds for both its conditional mean and variance estimation, representing the first result for variance estimation using ReLU networks. Furthermore, we develop a ReLU network based robust bootstrap procedure (Efron, 1992) for constructing confidence intervals for the true mean that comes with a theoretical guarantee on the coverage, providing a significant advancement in uncertainty quantification and the construction of reliable confidence intervals in deep learning settings.