Taking Human out of Learning Applications: A Survey on Automated Machine Learning

By Quanming Yao, Mengshuo Wang, Hugo Jair Escalante, Isabelle M Guyon, Yi-Qi Hu, Yu-Feng Li, Wei-Wei Tu, Qiang Yang, Yang Yu. 2018

216 citations recorded by the source corpus.

Subfields: Neural Architecture Search & AutoML

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Neural Architecture Search & AutoML

Citations in the corpus

In AI We Trust? Factors That Influence Trustworthiness of AI-infused Decision-Making Processes (2019, background): “Even though the performance of models created with automatedmethods can be on par (or exceed) that of models created by human data scientists [42], participants tended to be skeptical of the use of automated methods.”
Meta-Learning in Neural Networks: A Survey (2020, background): “AutoML: AutoML [31], [32] is a rather broad umbrella for approaches aiming to automate parts of the machine learning process that are typically manual, such as data preparation and cleaning, feature selection, algorithm selection, hyper-parameter tuning, architecture search, and so on.”
Generalizing from a Few Examples (2019, background): “Automated machine learning (AutoML) [153], by constructing task-aware machine learning models, has achieved state-of-the-art on many applications.”
Simplifying Architecture Search for Graph Neural Network (2020, background): “To address the above question, researchers turn to neural architecture search (NAS) [1, 6, 27, 32] approaches, which have shown promising results in automatically designing architectures for convolutional neural networks (CNN) and recurrent neural networks (RNN).”
Analysing the Overfit of the Auto-sklearn Automated Machine Learning Tool (2019, background): “Actually, a very recent and comprehensive survey of Auto-ML studies [9] does not even mention the issue of overﬁtting.”
Vertical Federated Learning: Concepts, Advances, and Challenges (2022, background): “Automated VFL: Automated machine learning (AutoML) is of great interest in alleviating human effort and achieving satisfactory model performance [193].”
Efficient Neural Architecture Search via Proximal Iterations (2019, methodology): “As NAS can be seen as a black-box optimization problem (Yao et al. 2018; Hutter, Kotthoff, and Vanschoren 2018), here, we bring the wisdom of constraint optimization to deal with the NAS problem.”