# Interval Regression: A Comparative Study with Proposed Models

arXiv: https://arxiv.org/abs/2503.02011 · HTML: https://mlsanity.com/paper/2503.02011/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Survival regression with accelerated failure time model in XGBoost](https://mlsanity.com/paper/2006.04920/) (2020)
- [Maximum Margin Interval Trees](https://mlsanity.com/paper/1710.04234/) (2017)
- [Weakly Supervised Regression with Interval Targets](https://mlsanity.com/paper/2306.10458/) (2023)
- [Penalty Learning for Optimal Partitioning using Multilayer Perceptron](https://mlsanity.com/paper/2408.00856/) (2024)

## Built on by (most cited first, 1 of 1)

- [Learning Penalty for Optimal Partitioning via Automatic Feature Extraction](https://mlsanity.com/paper/2505.07413/) (2025)

## Abstract (arXiv)

Regression models are essential for a wide range of real-world applications. However, in practice, target values are not always precisely known; instead, they may be represented as intervals of acceptable values. This challenge has led to the development of Interval Regression models. In this study, we provide a comprehensive review of existing Interval Regression models and introduce alternative models for comparative analysis. Experiments are conducted on both real-world and synthetic datasets to offer a broad perspective on model performance. The results demonstrate that no single model is universally optimal, highlighting the importance of selecting the most suitable model for each specific scenario.