# Consistent Multitask Learning with Nonlinear Output Relations

arXiv: https://arxiv.org/abs/1705.08118 · HTML: https://mlsanity.com/paper/1705.08118/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2017.

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

- [Fair Resource Allocation in Multi-Task Learning](https://mlsanity.com/paper/2402.15638/) (2024)
- [Fine-grained Generalization Analysis of Vector-valued Learning](https://mlsanity.com/paper/2104.14173/) (2021)
- [Cross-Task Consistency Learning Framework for Multi-Task Learning](https://mlsanity.com/paper/2111.14122/) (2021)
- [Key Design Choices for Double-Transfer in Source-Free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2302.05379/) (2023)
- [Robust-Multi-Task Gradient Boosting](https://mlsanity.com/paper/2507.11411/) (2025)
- [Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models](https://mlsanity.com/paper/2102.12942/) (2021)

## Abstract (arXiv)

Key to multitask learning is exploiting relationships between different tasks to improve prediction performance. If the relations are linear, regularization approaches can be used successfully. However, in practice assuming the tasks to be linearly related might be restrictive, and allowing for nonlinear structures is a challenge. In this paper, we tackle this issue by casting the problem within the framework of structured prediction. Our main contribution is a novel algorithm for learning multiple tasks which are related by a system of nonlinear equations that their joint outputs need to satisfy. We show that the algorithm is consistent and can be efficiently implemented. Experimental results show the potential of the proposed method.