  Learning with few labeled tabular samples is often an essential requirement
for industrial machine learning applications as varieties of tabular data
suffer from high annotation costs or have difficulties in collecting new
samples for novel tasks. Despite the utter importance, such a problem is quite
under-explored in the field of tabular learning, and existing few-shot learning
schemes from other domains are not straightforward to apply, mainly due to the
heterogeneous characteristics of tabular data. In this paper, we propose a
simple yet effective framework for few-shot semi-supervised tabular learning,
coined Self-generated Tasks from UNlabeled Tables (STUNT). Our key idea is to
self-generate diverse few-shot tasks by treating randomly chosen columns as a
target label. We then employ a meta-learning scheme to learn generalizable
knowledge with the constructed tasks. Moreover, we introduce an unsupervised
validation scheme for hyperparameter search (and early stopping) by generating
a pseudo-validation set using STUNT from unlabeled data. Our experimental
results demonstrate that our simple framework brings significant performance
gain under various tabular few-shot learning benchmarks, compared to prior
semi- and self-supervised baselines. Code is available at
https://github.com/jaehyun513/STUNT.
