maximum-mean-discrepancy

Introduces Smooth Maximum Mean Discrepancy (SMMD), a loss function that aligns predicted numeric distributions with targets using kernel matching and graph-based smoothness, improving numerical prediction accuracy in LLMs across multiple tasks.

This paper proposes CRUMB, a three-stage inference wrapper that clusters test queries and selects a distributionally matched training subset via MMD minimization to enable efficient Prior-Fitted Network inference on large datasets, achieving state-of-the-art context selection on 51 TabArena datasets.