# Skillearn: Machine Learning Inspired by Humans' Learning Skills

arXiv: https://arxiv.org/abs/2012.04863 · HTML: https://mlsanity.com/paper/2012.04863/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2020.

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

- [Learning from Mistakes -- A Framework for Neural Architecture Search](https://mlsanity.com/paper/2111.06353/) (2021)
- [Learning by Examples Based on Multi-level Optimization](https://mlsanity.com/paper/2109.10824/) (2021)
- [Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search](https://mlsanity.com/paper/2112.00275/) (2021)
- [Learning by Grouping: A Multilevel Optimization Framework for Improving Fairness in Classification without Losing Accuracy](https://mlsanity.com/paper/2304.00486/) (2023)

## Abstract (arXiv)

Humans, as the most powerful learners on the planet, have accumulated a lot of learning skills, such as learning through tests, interleaving learning, self-explanation, active recalling, to name a few. These learning skills and methodologies enable humans to learn new topics more effectively and efficiently. We are interested in investigating whether humans' learning skills can be borrowed to help machines to learn better. Specifically, we aim to formalize these skills and leverage them to train better machine learning (ML) models. To achieve this goal, we develop a general framework -- Skillearn, which provides a principled way to represent humans' learning skills mathematically and use the formally-represented skills to improve the training of ML models. In two case studies, we apply Skillearn to formalize two learning skills of humans: learning by passing tests and interleaving learning, and use the formalized skills to improve neural architecture search. Experiments on various datasets show that trained using the skills formalized by Skillearn, ML models achieve significantly better performance.