Survey on Automated Machine Learning

By Marc-André Zöller, Marco F. Huber. 2019

89 citations recorded by the source corpus.

Machine learning has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabling domain experts to automatically build machine learning applications without extensive knowledge of statistics and machine learning. In this survey, we summarize the recent developments in academy and industry regarding AutoML. First, we introduce a holistic problem formulation. Next, approaches for solving various subproblems of AutoML are presented. Finally, we provide an extensive empirical evaluation of the presented approaches on synthetic and real data.

Subfields: Neural Architecture Search & AutoML

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In Neural Architecture Search & AutoML

Citations in the corpus

AutoML: A Survey of the State-of-the-Art (2019, methodology): “The contributions of this paper are as follows: 1) Although there are several surveys related to AutoML (e.g. [3], [4], [33]), we cover a wider range of existing AutoML techniques according to the pipeline (Fig.”
In AI We Trust? Factors That Influence Trustworthiness of AI-infused Decision-Making Processes (2019, methodology): “Given the current popularity of automated training methods for AI (e.g., [11, 43]), we consider AI models trained by human data scientists vs. those trained by automated AI.”
AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model (2020, methodology): “1 Introduction Automatic machine learning (AutoML) has been studied to automate the process of data analytics to collect and integrate data, compose and optimise ML pipelines, and deploy and maintain predictive models [1,2,3].”
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization (2020, background): “…learning, where not only traditional hyperparameters such as learning rates (Zeiler, 2012), but also neural architectures (Liu et al., 2019), data augmentation policies (Cubuk et al., 2018) and other plausible variables are to be set automatically (Elsken et al., 2019; Zöller and Huber, 2019).”
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning (2020, methodology): “To alleviate this problem, we take advantage of automated machine learning (AutoML)(He et al., 2019; Z¨oller and Huber, 2019; Elshawi et al., 2019) to automatically adapt hyper parameters.”
How do Data Science Workers Collaborate? Roles, Workflows, and Tools (2020, background): “The meaning and nature of the data may be changed [23, 67] during this time-consuming step [34, 48, 67, 80, 103].”