Awesome-AutoML-Papers

Awesome-AutoML-Papers is a curated list of automated machine learning papers, articles, tutorials, slides and projects. Star this repository, and then you can keep abreast of the latest developments of this booming research field. Thanks to all the people who made contributions to this project. Join us and you are welcome to be a contributor.

What is AutoML?

Automated Machine Learning (AutoML) provides methods and processes to make Machine Learning available for non-Machine Learning experts, to improve efficiency of Machine Learning and to accelerate research on Machine Learning.

Machine Learning (ML) has achieved considerable successes in recent years and an ever-growing number of disciplines rely on it. However, this success crucially relies on human machine learning experts to perform the following tasks:

• Preprocess the data,
• Select appropriate features,
• Select an appropriate model family,
• Optimize model hyperparameters,
• Postprocess machine learning models,
• Critically analyze the results obtained.

As the complexity of these tasks is often beyond non-ML-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML. As a new sub-area in machine learning, AutoML has got more attention not only in machine learning but also in computer vision, natural language processing and graph computing.

There are no formal definition of AutoML. From the descriptions of most papers，the basic procedure of AutoML can be shown as the following.

AutoML approaches are already mature enough to rival and sometimes even outperform human machine learning experts. Put simply, AutoML can lead to improved performance while saving substantial amounts of time and money, as machine learning experts are both hard to find and expensive. As a result, commercial interest in AutoML has grown dramatically in recent years, and several major tech companies and start-up companies are now developing their own AutoML systems. An overview comparison of some of them can be summarized to the following table.

Company	AutoFE	HPO	NAS
4paradigm	√	√	×
Alibaba	×	√	×
Baidu	×	×	√
Determined AI	×	√	√
Google	√	√	√
DataCanvas	√	√	√
H2O.ai	√	√	×
Microsoft	×	√	√
MLJAR	√	√	√
RapidMiner	√	√	×
Tencent	×	√	×

Awesome-AutoML-Papers includes very up-to-date overviews of the bread-and-butter techniques we need in AutoML:

• Automated Data Clean (Auto Clean)
• Automated Feature Engineering (Auto FE)
• Hyperparameter Optimization (HPO)
• Meta-Learning
• Neural Architecture Search (NAS)

Table of Contents

• Papers
  • Surveys
  • Automated Feature Engineering
    • Expand Reduce
    • Hierarchical Organization of Transformations
    • Meta Learning
    • Reinforcement Learning
  • [Architecture Search](#architectur