Pretraining Dataset Diversity Impact on FLAT Accuracy in Tabular Few-Shot Learning with Column Permutation Noise

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 ge

Research goal: How does the choice of pretraining dataset diversity affect the accuracy of FLAT on benchmark tabular few-shot learning datasets (e.g., Tabular Few-Shot Benchmark) when tested under column permutation noise?

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