Unsupervised Generative Feature Transformation via Graph Contrastive Pre-training and Multi-objective Fine-tuning-Reference-Cited by-同舟云学术

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Unsupervised Generative Feature Transformation via Graph Contrastive Pre-training and Multi-objective Fine-tuning

Published:2024-08-24 Issue: Volume:10 Page:3966-3976
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Ying Wangyang¹^ORCID,Wang Dongjie²^ORCID,Hu Xuanming¹^ORCID,Zhou Yuanchun³^ORCID,Aggarwal Charu C.⁴^ORCID,Fu Yanjie¹^ORCID

Affiliation:

1. Arizona State University, Tempe, AZ, USA

2. The University of Kansas, Lawrence, KS, USA

3. Computer Network Information Center, Chinese Academy of Sciences, Beijing, China

4. International Business Machines T. J. Watson Research Center, Yorktown Heights, USA

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3672015

Reference38 articles.

1. AutoML: state of the art with a focus on anomaly detection, challenges, and research directions

2. Latent dirichlet allocation;Ng Y;Journal of machine Learning research 3,2003

3. Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607.

4. Neural Feature Search: A Neural Architecture for Automated Feature Engineering

5. Techniques for Automated Machine Learning

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