# Task Grouping for Automated Multi-Task Machine Learning via Task Affinity Prediction

arXiv: https://arxiv.org/abs/2310.16241 · HTML: https://mlsanity.com/paper/2310.16241/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 24 Oct 2023.

## Builds on (most cited first, 16 of 16)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Taskonomy: Disentangling Task Transfer Learning](https://mlsanity.com/paper/1804.08328/) (2018)
- [Which Tasks Should Be Learned Together in Multi-task Learning?](https://mlsanity.com/paper/1905.07553/) (2019)
- [Efficiently Identifying Task Groupings for Multi-Task Learning](https://mlsanity.com/paper/2109.04617/) (2021)
- [Multi-task Sequence to Sequence Learning](https://mlsanity.com/paper/1511.06114/) (2015)
- [Representation Similarity Analysis for Efficient Task taxonomy & Transfer Learning](https://mlsanity.com/paper/1904.11740/) (2019)
- [Identifying beneficial task relations for multi-task learning in deep neural networks](https://mlsanity.com/paper/1702.08303/) (2017)
- [The Information Complexity of Learning Tasks, their Structure and their Distance](https://mlsanity.com/paper/1904.03292/) (2019)
- [Zero-Shot Task Transfer](https://mlsanity.com/paper/1903.01092/) (2019)
- [Efficient Output Kernel Learning for Multiple Tasks](https://mlsanity.com/paper/1511.05706/) (2015)
- [Efficient and Scalable Multi-task Regression on Massive Number of Tasks](https://mlsanity.com/paper/1811.05695/) (2018)
- [On Better Exploring and Exploiting Task Relationships in Multi-Task Learning: Joint Model and Feature Learning](https://mlsanity.com/paper/1904.01747/) (2019)
- [Highly Scalable Task Grouping for Deep Multi-Task Learning in Prediction of Epigenetic Events](https://mlsanity.com/paper/2209.11892/) (2022)

## Built on by (most cited first, 4 of 4)

- [Scalable Multitask Learning Using Gradient-based Estimation of Task Affinity](https://mlsanity.com/paper/2409.06091/) (2024)
- [Graph Coloring for Multi-Task Learning](https://mlsanity.com/paper/2509.16959/) (2025)
- [Ensemble Prediction of Task Affinity for Efficient Multi-Task Learning](https://mlsanity.com/paper/2602.18591/) (2026)
- [Semantic-Aware Task Clustering for Constructive and Cooperative Multi-Tasking](https://mlsanity.com/paper/2607.21426/) (2026)

## Abstract (arXiv)

When a number of similar tasks have to be learned simultaneously, multi-task learning (MTL) models can attain significantly higher accuracy than single-task learning (STL) models. However, the advantage of MTL depends on various factors, such as the similarity of the tasks, the sizes of the datasets, and so on; in fact, some tasks might not benefit from MTL and may even incur a loss of accuracy compared to STL. Hence, the question arises: which tasks should be learned together? Domain experts can attempt to group tasks together following intuition, experience, and best practices, but manual grouping can be labor-intensive and far from optimal. In this paper, we propose a novel automated approach for task grouping. First, we study the affinity of tasks for MTL using four benchmark datasets that have been used extensively in the MTL literature, focusing on neural network-based MTL models. We identify inherent task features and STL characteristics that can help us to predict whether a group of tasks should be learned together using MTL or if they should be learned independently using STL. Building on this predictor, we introduce a randomized search algorithm, which employs the predictor to minimize the number of MTL trainings performed during the search for task groups. We demonstrate on the four benchmark datasets that our predictor-driven search approach can find better task groupings than existing baseline approaches.