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VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain
Jinsung Yoon · Yao Zhang · James Jordon · Mihaela van der Schaar

Mon Dec 07 09:00 PM -- 11:00 PM (PST) @ Poster Session 0 #25

Self- and semi-supervised learning frameworks have made significant progress in training machine learning models with limited labeled data in image and language domains. These methods heavily rely on the unique structure in the domain datasets (such as spatial relationships in images or semantic relationships in language). They are not adaptable to general tabular data which does not have the same explicit structure as image and language data. In this paper, we fill this gap by proposing novel self- and semi-supervised learning frameworks for tabular data, which we refer to collectively as VIME (Value Imputation and Mask Estimation). We create a novel pretext task of estimating mask vectors from corrupted tabular data in addition to the reconstruction pretext task for self-supervised learning. We also introduce a novel tabular data augmentation method for self- and semi-supervised learning frameworks. In experiments, we evaluate the proposed framework in multiple tabular datasets from various application domains, such as genomics and clinical data. VIME exceeds state-of-the-art performance in comparison to the existing baseline methods.

Author Information

Jinsung Yoon (Google)

I am a research scientist at Google Cloud AI. I am currently working on diverse machine learning research topics such as generative models, self- and semi-supervised learning, model interpretation, data imputation, and synthetic data generation. Previously, I worked on machine learning for medicine with Professor Mihaela van der Schaar as a graduate student researcher in UCLA Electrical and Computer Engineering Department. I received my Ph.D. and M.S. in Electrical and Computer Engineering Department at UCLA, and B.S. in Electrical and Computer Engineering at Seoul National University (SNU).

Yao Zhang (University of Cambridge)
James Jordon (University of Oxford)
Mihaela van der Schaar (University of Cambridge)

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