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arXiv · 2311.10051

Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces

Max Zhu·Katarzyna Kobalczyk·Andrija Petrovic·Mladen Nikolic·Mihaela van der Schaar·Boris Delibasic·Petro Lio
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Abstract

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.

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Max Zhu, Katarzyna Kobalczyk, Andrija Petrovic, Mladen Nikolic, Mihaela van der Schaar, Boris Delibasic, Petro Lio. 2023-11-16. Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces. https://arxiv.org/abs/2311.10051

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