AI RESEARCH
When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach
arXiv CS.AI
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ArXi:2605.19662v1 Announce Type: new Tabular foundation models based on pretrained prior-data fitted networks~(PFNs) have shown strong generalization on diverse tabular tasks, but they are typically designed for \emph{non-strategic} settings where data distributions are independent of deployed classifiers. In many real-world decision scenarios, however, individuals may strategically modify their features after deployment to obtain favorable outcomes, inducing a post-deployment distribution shift.