Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar
Abstract
Automatic machine learning is an important problem in the forefront of machine learning. The strongest AutoML systems are based on neural networks, evolutionary algorithms, and Bayesian optimization. Recently AlphaD3M reached state-of-the-art results with an order of magnitude speedup using reinforcement learning with self-play. In this work we extend AlphaD3M by using a pipeline grammar and a pre-trained model which generalizes from many different datasets and similar tasks. Our results demonstrate improved performance compared with our earlier work and existing methods on AutoML benchmark datasets for classification and regression tasks. In the spirit of reproducible research we make our data, models, and code publicly available.
Cite
@article{arxiv.1905.10345,
title = {Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar},
author = {Iddo Drori and Yamuna Krishnamurthy and Raoni Lourenco and Remi Rampin and Kyunghyun Cho and Claudio Silva and Juliana Freire},
journal= {arXiv preprint arXiv:1905.10345},
year = {2019}
}
Comments
ICML Workshop on Automated Machine Learning