Mitigating Label Shift in Tabular In-Context Learning via Test-Time Posterior Adjustment

ArXi:2605.04363v1 Announce Type: cross TabPFN has recently gained attention as a foundation model for tabular datasets, achieving strong performance by leveraging in-context learning on synthetic data. However, we find that TabPFN is vulnerable to label shift, often overfitting to the majority class in the