[en] 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.
Disciplines :
Computer science
Author, co-author :
Drori, Iddo
Krishnamurthy, Yamuna
DE PAULA LOURENCO, Raoni ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal ; NYU - New York University [US-NY]
Rampin, Remi
Cho, Kyunghyun
Silva, Claudio
Freire, Juliana
External co-authors :
yes
Language :
English
Title :
Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar