# Automated Machine Learning with Monte-Carlo Tree Search

arXiv: https://arxiv.org/abs/1906.00170 · HTML: https://mlsanity.com/paper/1906.00170/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2019.

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

- [ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoning](https://mlsanity.com/paper/2506.16499/) (2025)
- [Automating Data Science: Prospects and Challenges](https://mlsanity.com/paper/2105.05699/) (2021)
- [Naive Automated Machine Learning](https://mlsanity.com/paper/2111.14514/) (2021)
- [A Scalable AutoML Approach Based on Graph Neural Networks](https://mlsanity.com/paper/2111.00083/) (2021)
- [TransAxx: Efficient Transformers with Approximate Computing](https://mlsanity.com/paper/2402.07545/) (2024)
- [Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization](https://mlsanity.com/paper/2206.01409/) (2022)
- [Using Sequential Statistical Tests for Efficient Hyperparameter Tuning](https://mlsanity.com/paper/2112.12438/) (2021)
- [Naive Automated Machine Learning -- A Late Baseline for AutoML](https://mlsanity.com/paper/2103.10496/) (2021)

## Abstract (arXiv)

The AutoML task consists of selecting the proper algorithm in a machine learning portfolio, and its hyperparameter values, in order to deliver the best performance on the dataset at hand. Mosaic, a Monte-Carlo tree search (MCTS) based approach, is presented to handle the AutoML hybrid structural and parametric expensive black-box optimization problem. Extensive empirical studies are conducted to independently assess and compare: i) the optimization processes based on Bayesian optimization or MCTS; ii) its warm-start initialization; iii) the ensembling of the solutions gathered along the search. Mosaic is assessed on the OpenML 100 benchmark and the Scikit-learn portfolio, with statistically significant gains over Auto-Sklearn, winner of former international AutoML challenges.