New research revisits deep learning regression objectives for tabular data

A new research paper revisits deep learning regression objectives for tabular data, finding that while Quantile Loss offers consistent improvements over Mean Squared Error (MSE), a single global early stopping point can still limit performance. The study introduces a heteroskedasticity-aware extension of quantile loss to address this limitation by using input-dependent label smoothing. The research provides an updated empirical view of regression objectives for tabular deep learning and makes its code available on Hugging Face. AI

IMPACT Provides updated empirical insights into regression objectives for tabular deep learning, potentially improving model performance in these common scenarios.

RANK_REASON Academic paper on machine learning techniques. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
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• DagsHub
• Gotit.pub
• Hugging Face
• IArxiv
• Mean Squared Error
• Quantile Loss
• ScienceCast

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