# Lale: Consistent Automated Machine Learning

arXiv: https://arxiv.org/abs/2007.01977 · HTML: https://mlsanity.com/paper/2007.01977/

## Key facts

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

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

- [The Intersectionality Problem for Algorithmic Fairness](https://mlsanity.com/paper/2411.02569/) (2024)
- [Learning GraphQL Query Costs (Extended Version)](https://mlsanity.com/paper/2108.11139/) (2021)
- [Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE)](https://mlsanity.com/paper/2202.10979/) (2022)
- [Toward Theoretical Guidance for Two Common Questions in Practical Cross-Validation based Hyperparameter Selection](https://mlsanity.com/paper/2301.05131/) (2023)

## Abstract (arXiv)

Automated machine learning makes it easier for data scientists to develop pipelines by searching over possible choices for hyperparameters, algorithms, and even pipeline topologies. Unfortunately, the syntax for automated machine learning tools is inconsistent with manual machine learning, with each other, and with error checks. Furthermore, few tools support advanced features such as topology search or higher-order operators. This paper introduces Lale, a library of high-level Python interfaces that simplifies and unifies automated machine learning in a consistent way.