• Corpus ID: 228084288

Ensemble Squared: A Meta AutoML System

  title={Ensemble Squared: A Meta AutoML System},
  author={Jason Yoo and Tony Joseph and Dylan Yung and Seyed Ali Nasseri and Frank D. Wood},
The continuing rise in the number of problems amenable to machine learning solutions, coupled with simultaneous growth in both computing power and variety of machine learning techniques has led to an explosion of interest in automated machine learning (AutoML). This paper presents Ensemble Squared (Ensemble$^2$), a "meta" AutoML system that ensembles at the level of AutoML systems. Ensemble$^2$ exploits the diversity of existing, competing AutoML systems by ensembling the top-performing models… 

Figures and Tables from this paper

A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features

This work proposes a robust stacking framework that fuses graph-aware propagation with arbitrary models intended for IID data, which are ensembled and stacked in multiple layers and leverages bagging and stacking strategies to enjoy strong generalization, in a manner which effectively mitigates label leakage and overfitting.

Benchmarking Multimodal AutoML for Tabular Data with Text Fields

This benchmark considers the use of automated supervised learning systems for data tables that not only contain numeric/categorical columns, but one or more text fields as well, and evaluates various straightforward pipelines to model such data.

Multimodal AutoML on Structured Tables with Text Fields

This work designs automated supervised learning systems for data tables that not only contain numeric/categorical columns, but text fields as well, and proposes various practically superior strategies based on multimodal adaptations of Transformer networks and stack ensembling of these networks with classical tabular models.

An Ontology-Based Concept for Meta AutoML

OMA-ML is a concept called OMA-ML (Ontology-based Meta AutoML) that combines the strengths of existing AutoML solutions by integrating them (meta AutoML).



AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning models on an unprocessed tabular dataset such as a…

An Open Source AutoML Benchmark

An open, ongoing, and extensible benchmark framework which follows best practices and avoids common mistakes is introduced which is used to conduct a thorough comparison of 4 AutoML systems across 39 datasets and analyze the results.

Efficient and Robust Automated Machine Learning

This work introduces a robust new AutoML system based on scikit-learn, which improves on existing AutoML methods by automatically taking into account past performance on similar datasets, and by constructing ensembles from the models evaluated during the optimization.

Scikit-learn: Machine Learning in Python

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing…

DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering

This study presents DeepLine, a reinforcement learning-based approach for automatic pipeline generation that utilizes an efficient representation of the search space together with a novel method for operating in environments with large and dynamic action spaces.

Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar

This work extends AlphaD3M by using a pipeline grammar and a pre-trained model which generalizes from many different datasets and similar tasks and demonstrates improved performance compared with earlier work and existing methods on AutoML benchmark datasets for classification and regression tasks.

Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA

The new version of Auto-WEKA is described, a system designed to help novice users by automatically searching through the joint space of WEKA's learning algorithms and their respective hyperparameter settings to maximize performance, using a state-of-the-art Bayesian optimization method.

H2O AutoML: Scalable Automatic Machine Learning

H2O AutoML is presented, a highly scalable, fully-automated, supervised learning algorithm which automates the process of training a large selection of candidate models and stacked ensembles within a single function.

Auto-Sklearn 2.0: The Next Generation

This paper extends Auto-sklearn with a new, simpler meta-learning technique, improves its way of handling iterative algorithms and enhance it with a successful bandit strategy for budget allocation, and proposes a solution towards truly hand-free AutoML.

Neural Ensemble Search for Performant and Calibrated Predictions

This work investigates neural architecture search (NAS) for explicitly constructing ensembles to exploit diversity among networks of varying architectures and to achieve robustness against distributional shift, and finds that the resulting ensemble are more diverse compared to ensemble composed of a fixed architecture and are therefore also more powerful.