# Multi-group Learning for Hierarchical Groups

arXiv: https://arxiv.org/abs/2402.00258 · HTML: https://mlsanity.com/paper/2402.00258/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 43 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 30 of 30)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021)
- [Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification](https://mlsanity.com/paper/1903.04561/) (2019)
- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Fairness without Demographics through Adversarially Reweighted Learning](https://mlsanity.com/paper/2006.13114/) (2020)
- [Multiaccuracy: Black-Box Post-Processing for Fairness in Classification](https://mlsanity.com/paper/1805.12317/) (2018)
- [Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging](https://mlsanity.com/paper/1909.12475/) (2019)
- [No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems](https://mlsanity.com/paper/2011.12945/) (2020)
- [Domino: Discovering Systematic Errors with Cross-Modal Embeddings](https://mlsanity.com/paper/2203.14960/) (2022)
- [Does Object Recognition Work for Everyone?](https://mlsanity.com/paper/1906.02659/) (2019)
- [Outcome Indistinguishability](https://mlsanity.com/paper/2011.13426/) (2020)
- [Automated Data Slicing for Model Validation:A Big data - AI Integration Approach](https://mlsanity.com/paper/1807.06068/) (2018)
- [Multi-group Agnostic PAC Learnability](https://mlsanity.com/paper/2105.09989/) (2021)
- [On-Demand Sampling: Learning Optimally from Multiple Distributions](https://mlsanity.com/paper/2210.12529/) (2022)
- [Multicalibration as Boosting for Regression](https://mlsanity.com/paper/2301.13767/) (2023)
- [(De)Constructing Bias on Skin Lesion Datasets](https://mlsanity.com/paper/1904.08818/) (2019)
- [Advancing subgroup fairness via sleeping experts](https://mlsanity.com/paper/1909.08375/) (2019)
- [Simple and near-optimal algorithms for hidden stratification and multi-group learning](https://mlsanity.com/paper/2112.12181/) (2021)
- [Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation](https://mlsanity.com/paper/2010.10363/) (2020)
- [An Algorithmic Framework for Bias Bounties](https://mlsanity.com/paper/2201.10408/) (2022)
- [Identification of Negative Transfers in Multitask Learning Using Surrogate Models](https://mlsanity.com/paper/2303.14582/) (2023)
- [Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation](https://mlsanity.com/paper/2211.12703/) (2022)
- [Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text](https://mlsanity.com/paper/2110.08228/) (2021)
- [An adaptive nearest neighbor rule for classification](https://mlsanity.com/paper/1905.12717/) (2019)
- [A comparison of approaches to improve worst-case predictive model performance over patient subpopulations](https://mlsanity.com/paper/2108.12250/) (2021)
- [Gender-From-Iris or Gender-From-Mascara?](https://mlsanity.com/paper/1702.01304/) (2017)
- [Agnostic Multi-Group Active Learning](https://mlsanity.com/paper/2306.01922/) (2023)

## Built on by (most cited first, 8 of 8)

- [Sample-Efficient Omniprediction for Proper Losses](https://mlsanity.com/paper/2510.12769/) (2025)
- [Panprediction: Optimal Predictions for Any Downstream Task and Loss](https://mlsanity.com/paper/2510.27638/) (2025)
- [Efficient Estimation of Kernel Surrogate Models for Task Attribution](https://mlsanity.com/paper/2602.03783/) (2026)
- [Group-realizable multi-group learning by minimizing empirical risk](https://mlsanity.com/paper/2601.16922/) (2026)
- [Learning Tree-Structured Composition of Data Augmentation](https://mlsanity.com/paper/2408.14381/) (2024)
- [ShakyPrepend: A Multi-Group Learner with Improved Sample Complexity](https://mlsanity.com/paper/2603.07319/) (2026)
- [A One-Inclusion Graph Approach to Multi-Group Learning](https://mlsanity.com/paper/2603.23208/) (2026)
- [Sample-Optimal Multi-Group Learning with an ERM Oracle](https://mlsanity.com/paper/2610.15916/) (2026)

## Abstract (arXiv)

The multi-group learning model formalizes the learning scenario in which a single predictor must generalize well on multiple, possibly overlapping subgroups of interest. We extend the study of multi-group learning to the natural case where the groups are hierarchically structured. We design an algorithm for this setting that outputs an interpretable and deterministic decision tree predictor with near-optimal sample complexity. We then conduct an empirical evaluation of our algorithm and find that it achieves attractive generalization properties on real datasets with hierarchical group structure.