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Transfer Learning with Deep Tabular Models
Roman Levin · Valeriia Cherepanova · Avi Schwarzschild · Arpit Bansal · C. Bayan Bruss · Tom Goldstein · Andrew Wilson · Micah Goldblum
Event URL: https://openreview.net/forum?id=FUMxFwyLQZ »

Recent work on deep learning for tabular data demonstrates the strong performance of deep tabular models, often bridging the gap between gradient boosted decision trees and neural networks. Accuracy aside, a major advantage of neural models is that they are easily fine-tuned in new domains and learn reusable features. This property is often exploited in computer vision and natural language applications, where transfer learning is indispensable when task-specific training data is scarce. In this work, we explore the benefits that representation learning provides for knowledge transfer in the tabular domain. We conduct experiments in a realistic medical diagnosis test bed with limited amounts of downstream data and find that transfer learning with deep tabular models provides a definitive advantage over gradient boosted decision tree methods. We further compare the supervised and self-supervised pretraining strategies and provide practical advice on transfer learning with tabular models. Finally, we propose a pseudo-feature method for cases where the upstream and downstream feature sets differ, a tabular-specific problem widespread in real-world applications.

Author Information

Roman Levin (Amazon)
Valeriia Cherepanova (University of Maryland)
Avi Schwarzschild (University of Maryland)
Arpit Bansal (University of Maryland, College Park)
C. Bayan Bruss (Capital One)
Tom Goldstein (University of Maryland)
Andrew Wilson (New York University)
Andrew Wilson

I am a professor of machine learning at New York University.

Micah Goldblum (University of Maryland)

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