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Proposes a continuity criterion for extending discrete-time causal prior-data fitted networks to continuous time using stochastic differential equations, introducing a taxonomy and fine-grid integration method that outperforms naive integration on irregular observation schedules.
This paper adapts classical class imbalance techniques to Prior-Data Fitted Networks (PFNs) for tabular classification, finding that thresholding and downsampling perform well due to PFNs' calibration and limited-data capabilities.
TabPFN-MT extends PFNs to multitask in-context learning for tabular data, achieving state-of-the-art on small-to-medium datasets while reducing inference cost from O(T) to O(1) forward passes.