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TabCon is developed, an amortized inference system built on a tabular foundation model that produces confidence intervals for new datasets through a simple forward pass, and offers considerably greater computational efficiency than the classical bootstrap procedure.
The key idea is to smooth adjacent token scores to reduce their variability, while using an adaptive Lepski-type rule to select the bandwidth according to the local authorship structure, and the proposed method achieves favorable mean square error performance in estimating the underlying signal.
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