Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces
Max Zhu Katarzyna Kobalczyk Andrija Petrovic Mladen Nikolic Mihaela van der Schaar Boris Delibasic Petro Lio
Despite the prevalence of tabular datasets, few-shot learning remains
under-explored within this domain. Existing few-shot methods are not directly
applicable to tabular datasets due to varying column relationships, meanings,
and permutational invariance. To address these challenges, we propose FLAT-a
novel approach to tabular few-shot learning, encompassing knowledge sharing
between datasets with heterogeneous feature spaces. Utilizing an encoder
inspired by Dataset2Vec, FLAT learns low-dimensional embeddings of datasets and
their individual columns, which facilitate knowledge transfer and
generalization to previously unseen datasets. A decoder network parametrizes
the predictive target network, implemented as a Graph Attention Network, to
accommodate the heterogeneous nature of tabular datasets. Experiments on a
diverse collection of 118 UCI datasets demonstrate FLAT's successful
generalization to new tabular datasets and a considerable improvement over the
baselines.