# Weakly Supervised Regression with Interval Targets

arXiv: https://arxiv.org/abs/2306.10458 · HTML: https://mlsanity.com/paper/2306.10458/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 18 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [Delving into Deep Imbalanced Regression](https://mlsanity.com/paper/2102.09554/) (2021)
- [Machine Learning for Survival Analysis: A Survey](https://mlsanity.com/paper/1708.04649/) (2017)
- [Progressive Identification of True Labels for Partial-Label Learning](https://mlsanity.com/paper/2002.08053/) (2020)
- [Provably Consistent Partial-Label Learning](https://mlsanity.com/paper/2007.08929/) (2020)
- [Complementary-Label Learning for Arbitrary Losses and Models](https://mlsanity.com/paper/1810.04327/) (2018)
- [Maximum Margin Interval Trees](https://mlsanity.com/paper/1710.04234/) (2017)
- [Learning relationships between data obtained independently](https://mlsanity.com/paper/1601.00504/) (2016)
- [Uncoupled Regression from Pairwise Comparison Data](https://mlsanity.com/paper/1905.13659/) (2019)

## Built on by (most cited first, 6 of 6)

- [Improve Representation for Imbalanced Regression through Geometric Constraints](https://mlsanity.com/paper/2503.00876/) (2025)
- [SupReMix: Supervised Contrastive Learning for Medical Imaging Regression with Mixup](https://mlsanity.com/paper/2309.16633/) (2023)
- [Interval Regression: A Comparative Study with Proposed Models](https://mlsanity.com/paper/2503.02011/) (2025)
- [Learning from Interval Targets](https://mlsanity.com/paper/2510.20925/) (2025)
- [Learning from Complementary Features](https://mlsanity.com/paper/2408.14788/) (2024)
- [Uncertainty-Aware Learning from Multi-Expert Interval Targets](https://mlsanity.com/paper/2610.00102/) (2026)

## Abstract (arXiv)

This paper investigates an interesting weakly supervised regression setting called regression with interval targets (RIT). Although some of the previous methods on relevant regression settings can be adapted to RIT, they are not statistically consistent, and thus their empirical performance is not guaranteed. In this paper, we provide a thorough study on RIT. First, we proposed a novel statistical model to describe the data generation process for RIT and demonstrate its validity. Second, we analyze a simple selection method for RIT, which selects a particular value in the interval as the target value to train the model. Third, we propose a statistically consistent limiting method for RIT to train the model by limiting the predictions to the interval. We further derive an estimation error bound for our limiting method. Finally, extensive experiments on various datasets demonstrate the effectiveness of our proposed method.