  In real-world applications, tabular data often suffer from distribution
shifts due to their widespread and abundant nature, leading to erroneous
predictions of pre-trained machine learning models. However, addressing such
distribution shifts in the tabular domain has been relatively underexplored due
to unique challenges such as varying attributes and dataset sizes, as well as
the limited representation learning capabilities of deep learning models for
tabular data. Particularly, with the recent promising paradigm of test-time
adaptation (TTA), where we adapt the off-the-shelf model to the unlabeled
target domain during the inference phase without accessing the source domain,
we observe that directly adopting commonly used TTA methods from other domains
often leads to model collapse. We systematically explore challenges in tabular
data test-time adaptation, including skewed entropy, complex latent space
decision boundaries, confidence calibration issues with both overconfident and
under-confident, and model bias towards source label distributions along with
class imbalances. Based on these insights, we introduce AdapTable, a novel
tabular test-time adaptation method that directly modifies output probabilities
by estimating target label distributions and adjusting initial probabilities
based on calibrated uncertainty. Extensive experiments on both natural
distribution shifts and synthetic corruptions demonstrate the adaptation
efficacy of the proposed method.
