# Exploring Loss Design Techniques For Decision Tree Robustness To Label Noise

arXiv: https://arxiv.org/abs/2405.17672 · HTML: https://mlsanity.com/paper/2405.17672/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 6 of 6)

- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021)
- [A Survey of Label-noise Representation Learning: Past, Present and Future](https://mlsanity.com/paper/2011.04406/) (2020)
- [On Symmetric Losses for Learning from Corrupted Labels](https://mlsanity.com/paper/1901.09314/) (2019)
- [Double Ramp Loss Based Reject Option Classifier](https://mlsanity.com/paper/1311.6556/) (2013)

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

- [Decomposing Observational Multiplicity in Decision Trees: Leaf and Structural Regret](https://mlsanity.com/paper/2603.11701/) (2026)

## Abstract (arXiv)

In the real world, data is often noisy, affecting not only the quality of features but also the accuracy of labels. Current research on mitigating label errors stems primarily from advances in deep learning, and a gap exists in exploring interpretable models, particularly those rooted in decision trees. In this study, we investigate whether ideas from deep learning loss design can be applied to improve the robustness of decision trees. In particular, we show that loss correction and symmetric losses, both standard approaches, are not effective. We argue that other directions need to be explored to improve the robustness of decision trees to label noise.