  On tabular data, a significant body of literature has shown that current deep
learning (DL) models perform at best similarly to Gradient Boosted Decision
Trees (GBDTs), while significantly underperforming them on outlier data.
However, these works often study idealized problem settings which may fail to
capture complexities of real-world scenarios. We identify a natural tabular
data setting where DL models can outperform GBDTs: tabular Learning-to-Rank
(LTR) under label scarcity. Tabular LTR applications, including search and
recommendation, often have an abundance of unlabeled data, and scarce labeled
data. We show that DL rankers can utilize unsupervised pretraining to exploit
this unlabeled data. In extensive experiments over both public and proprietary
datasets, we show that pretrained DL rankers consistently outperform GBDT
rankers on ranking metrics -- sometimes by as much as 38% -- both overall and
on outliers.
