# SubStrat: A Subset-Based Strategy for Faster AutoML

arXiv: https://arxiv.org/abs/2206.03070 · HTML: https://mlsanity.com/paper/2206.03070/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 54 references that are in the map.
- First posted on arXiv 7 Jun 2022.

## Builds on (most cited first, 21 of 21)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning](https://mlsanity.com/paper/2012.10630/) (2020)
- [OpenML Benchmarking Suites](https://mlsanity.com/paper/1708.03731/) (2017)
- [Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning](https://mlsanity.com/paper/2007.04074/) (2020)
- [AutoML-Zero: Evolving Machine Learning Algorithms From Scratch](https://mlsanity.com/paper/2003.03384/) (2020)
- [FLAML: A Fast and Lightweight AutoML Library](https://mlsanity.com/paper/1911.04706/) (2019)
- [An Open Source AutoML Benchmark](https://mlsanity.com/paper/1907.00909/) (2019)
- [Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science](https://mlsanity.com/paper/1603.06212/) (2016)
- [AutoML to Date and Beyond: Challenges and Opportunities](https://mlsanity.com/paper/2010.10777/) (2020)
- [VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition](https://mlsanity.com/paper/2107.08861/) (2021)
- [AlphaD3M: Machine Learning Pipeline Synthesis](https://mlsanity.com/paper/2111.02508/) (2021)
- [DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering](https://mlsanity.com/paper/1911.00061/) (2019)
- [SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle](https://mlsanity.com/paper/1909.02976/) (2019)
- [Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic Study](https://mlsanity.com/paper/1509.04210/) (2015)
- [Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads](https://mlsanity.com/paper/1708.07308/) (2017)
- [Auto-Meta: Automated Gradient Based Meta Learner Search](https://mlsanity.com/paper/1806.06927/) (2018)
- [Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning](https://mlsanity.com/paper/2011.05231/) (2020)
- [A Scalable AutoML Approach Based on Graph Neural Networks](https://mlsanity.com/paper/2111.00083/) (2021)
- [Visualization-Aware Sampling for Very Large Databases](https://mlsanity.com/paper/1510.03921/) (2015)
- [Auto-Model: Utilizing Research Papers and HPO Techniques to Deal with the CASH problem](https://mlsanity.com/paper/1910.10902/) (2019)

## Built on by (most cited first, 8 of 8)

- [Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey](https://mlsanity.com/paper/2411.10478/) (2024)
- [A Comprehensive Benchmark of Machine and Deep Learning Across Diverse Tabular Datasets](https://mlsanity.com/paper/2408.14817/) (2024)
- [Symbolic Regression as Feature Engineering Method for Machine and Deep Learning Regression Tasks](https://mlsanity.com/paper/2311.06028/) (2023)
- [Interactive Data Harmonization with LLM Agents: Opportunities and Challenges](https://mlsanity.com/paper/2502.07132/) (2025)
- [Machine Learning Approaches to Predict and Detect Early-Onset of Digital Dermatitis in Dairy Cows using Sensor Data](https://mlsanity.com/paper/2309.10010/) (2023)
- [Exploration-Exploitation Model of Moth-Inspired Olfactory Navigation](https://mlsanity.com/paper/2312.11492/) (2023)
- [Introducing 'Inside' Out of Distribution](https://mlsanity.com/paper/2407.04534/) (2024)
- [GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization](https://mlsanity.com/paper/2604.11810/) (2026)

## Abstract (arXiv)

Automated machine learning (AutoML) frameworks have become important tools in the data scientists' arsenal, as they dramatically reduce the manual work devoted to the construction of ML pipelines. Such frameworks intelligently search among millions of possible ML pipelines - typically containing feature engineering, model selection and hyper parameters tuning steps - and finally output an optimal pipeline in terms of predictive accuracy. However, when the dataset is large, each individual configuration takes longer to execute, therefore the overall AutoML running times become increasingly high. To this end, we present SubStrat, an AutoML optimization strategy that tackles the data size, rather than configuration space. It wraps existing AutoML tools, and instead of executing them directly on the entire dataset, SubStrat uses a genetic-based algorithm to find a small yet representative data subset which preserves a particular characteristic of the full data. It then employs the AutoML tool on the small subset, and finally, it refines the resulted pipeline by executing a restricted, much shorter, AutoML process on the large dataset. Our experimental results, performed on two popular AutoML frameworks, Auto-Sklearn and TPOT, show that SubStrat reduces their running times by 79% (on average), with less than 2% average loss in the accuracy of the resulted ML pipeline.