GIST: Generator-state-Informed Supervision for Tabular Foundation Models
Tabular foundation models (TFMs) based on prior-data fitted networks typically use sampled hard query labels, leaving avoidable sampling variability in pretraining. A key observation is that intermediate generator states can reveal label uncertainty beyond a sampled outcome. We propose Generator-state-Informed Supervis...
Zhang-Yang Peng, Jing-Ze Shi, Tong-Yi Zhang et al.
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