EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks

• URL: http://arxiv.org/abs/2502.06684v1
• Date: Mon, 10 Feb 2025 17:11:20 GMT
• Title: EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks
• Authors: Michael Arbel, David Salinas, Frank Hutter,
• Abstract summary: In this study, we identify this oversight as a source of incompressible error, termed the equivariance gap, which introduces instability in predictions. To mitigate these issues, we propose a novel model designed to preserve equivariance across output dimensions.
• Score: 55.214444066134114
• License:
• Abstract: Recent foundational models for tabular data, such as TabPFN, have demonstrated remarkable effectiveness in adapting to new tasks through in-context learning. However, these models overlook a crucial equivariance property: the arbitrary ordering of target dimensions should not influence model predictions. In this study, we identify this oversight as a source of incompressible error, termed the equivariance gap, which introduces instability in predictions. To mitigate these issues, we propose a novel model designed to preserve equivariance across output dimensions. Our experimental results indicate that our proposed model not only addresses these pitfalls effectively but also achieves competitive benchmark performance.

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