Fully Test-time Adaptation for Tabular Data

TL;DR

This paper introduces FTAT, a novel method for fully test-time adaptation of deep models on tabular data, addressing distribution shifts and improving robustness during testing.

Contribution

We propose FTAT, a new approach that effectively handles label and covariate shifts in tabular data during test-time adaptation, outperforming existing methods.

Findings

FTAT outperforms state-of-the-art methods on six benchmark datasets.

FTAT effectively adapts to distribution shifts in various tasks.

Experimental results show significant performance improvements.

Abstract

Tabular data plays a vital role in various real-world scenarios and finds extensive applications. Although recent deep tabular models have shown remarkable success, they still struggle to handle data distribution shifts, leading to performance degradation when testing distributions change. To remedy this, a robust tabular model must adapt to generalize to unknown distributions during testing. In this paper, we investigate the problem of fully test-time adaptation (FTTA) for tabular data, where the model is adapted using only the testing data. We identify three key challenges: the existence of label and covariate distribution shifts, the lack of effective data augmentation, and the sensitivity of adaptation, which render existing FTTA methods ineffective for tabular data. To this end, we propose the Fully Test-time Adaptation for Tabular data, namely FTAT, which enables FTTA methods to robustly optimize the label distribution of predictions, adapt to shifted covariate distributions, and suit a variety of tasks and models effectively. We conduct comprehensive experiments on six benchmark datasets, which are evaluated using three metrics. The experimental results demonstrate that FTAT outperforms state-of-the-art methods by a margin.