TabPFN Fairness-Accuracy Trade-offs with Sparse vs. Dense SCM Pre-training

Abstract

Abstract: One of the most challenging problems in graph machine learning is generalizing across graphs with diverse properties. Graph neural networks (GNNs) face a fundamental limitation: they require separate training for each new graph, preventing universal generalization across diverse graph datasets. A critical challenge facing GNNs lies in their reliance on labeled training data for each individual graph, a requirement that hinders the capacity for universal node classification due to the heterogeneity inherent in graphs – differences in homophily levels, community structures, and feature distribu

Research Question

What is the impact of pre-training with sparse vs. dense SCM features on TabPFN's fairness-accuracy trade-off, and how does this compare to fairness-accuracy trade-offs observed in other foundational models like PaLM or Llama 2?

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