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Quantile loss consistently beats MSE on tabular regression, but global early stopping still compromises performance in noisy regions, necessitating input-dependent label smoothing.
Achieving competitive performance on tabular tasks without the burden of hyperparameter tuning, TabPack drastically cuts down training time and resource usage.
Ditch AdamW? Muon optimizer consistently outperforms it for tabular MLPs, offering a new default for practitioners willing to trade off training efficiency.