# Naive Automated Machine Learning

arXiv: https://arxiv.org/abs/2111.14514 · HTML: https://mlsanity.com/paper/2111.14514/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 15 of 15)

- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Non-stochastic Best Arm Identification and Hyperparameter Optimization](https://mlsanity.com/paper/1502.07943/) (2015)
- [An Open Source AutoML Benchmark](https://mlsanity.com/paper/1907.00909/) (2019)
- [Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI](https://mlsanity.com/paper/1909.02309/) (2019)
- [Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly](https://mlsanity.com/paper/1903.06694/) (2019)
- [An ADMM Based Framework for AutoML Pipeline Configuration](https://mlsanity.com/paper/1905.00424/) (2019)
- [Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems](https://mlsanity.com/paper/2001.06509/) (2020)
- [Probabilistic Matrix Factorization for Automated Machine Learning](https://mlsanity.com/paper/1705.05355/) (2017)
- [OBOE: Collaborative Filtering for AutoML Model Selection](https://mlsanity.com/paper/1808.03233/) (2018)
- [BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters](https://mlsanity.com/paper/1908.06756/) (2019)
- [Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop](https://mlsanity.com/paper/2101.04296/) (2021)
- [Autostacker: A Compositional Evolutionary Learning System](https://mlsanity.com/paper/1803.00684/) (2018)
- [Automated Machine Learning with Monte-Carlo Tree Search](https://mlsanity.com/paper/1906.00170/) (2019)

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

- [TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications](https://mlsanity.com/paper/2311.02971/) (2023)
- [Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals](https://mlsanity.com/paper/2412.13312/) (2024)
- [When Plants Respond: Electrophysiology and Machine Learning for Green Monitoring Systems](https://mlsanity.com/paper/2506.23872/) (2025)
- [Imbalanced Regression Pipeline Recommendation](https://mlsanity.com/paper/2507.11901/) (2025)
- [Classifying Subjective Time Perception in a Multi-robot Control Scenario Using Eye-tracking Information](https://mlsanity.com/paper/2504.06442/) (2025)
- [Early Detection of Water Stress by Plant Electrophysiology: Machine Learning for Irrigation Management](https://mlsanity.com/paper/2604.28038/) (2026)
- [Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimization](https://mlsanity.com/paper/2606.04866/) (2026)
- [Large Language Models for Automated Cross-Domain Machine Learning Task Type Identification: A Benchmark Dataset and Evaluation](https://mlsanity.com/paper/2609.35335/) (2026)

## Abstract (arXiv)

An essential task of Automated Machine Learning (AutoML) is the problem of automatically finding the pipeline with the best generalization performance on a given dataset. This problem has been addressed with sophisticated black-box optimization techniques such as Bayesian Optimization, Grammar-Based Genetic Algorithms, and tree search algorithms. Most of the current approaches are motivated by the assumption that optimizing the components of a pipeline in isolation may yield sub-optimal results. We present Naive AutoML, an approach that does precisely this: It optimizes the different algorithms of a pre-defined pipeline scheme in isolation. The finally returned pipeline is obtained by just taking the best algorithm of each slot. The isolated optimization leads to substantially reduced search spaces, and, surprisingly, this approach yields comparable and sometimes even better performance than current state-of-the-art optimizers.