# Constraint-driven multi-task learning

arXiv: https://arxiv.org/abs/2208.11656 · HTML: https://mlsanity.com/paper/2208.11656/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Builds on (most cited first, 8 of 8)

- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)
- [Building machines that adapt and compute like brains](https://mlsanity.com/paper/1711.04203/) (2017)
- [Learning programs by learning from failures](https://mlsanity.com/paper/2005.02259/) (2020)
- [Inductive logic programming at 30](https://mlsanity.com/paper/2102.10556/) (2021)
- [A Survey on Curriculum Learning](https://mlsanity.com/paper/2010.13166/) (2020)
- [Forgetting to learn logic programs](https://mlsanity.com/paper/1911.06643/) (2019)
- [Knowledge Transfer Between Artificial Intelligence Systems](https://mlsanity.com/paper/1709.01547/) (2017)
- [Learning Logic Programs From Noisy Failures](https://mlsanity.com/paper/2201.03702/) (2021)

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

- [Learning logic programs by discovering higher-order abstractions](https://mlsanity.com/paper/2308.08334/) (2023)

## Abstract (arXiv)

Inductive logic programming is a form of machine learning based on mathematical logic that generates logic programs from given examples and background knowledge. In this project, we extend the Popper ILP system to make use of multi-task learning. We implement the state-of-the-art approach and several new strategies to improve search performance. Furthermore, we introduce constraint preservation, a technique that improves overall performance for all approaches. Constraint preservation allows the system to transfer knowledge between updates on the background knowledge set. Consequently, we reduce the amount of repeated work performed by the system. Additionally, constraint preservation allows us to transition from the current state-of-the-art iterative deepening search approach to a more efficient breadth first search approach. Finally, we experiment with curriculum learning techniques and show their potential benefit to the field.