Author

Jingpu Cheng

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#machine learning Preprint Nov 2026

Point interpolation and distributional universality in flow-based generative models

This work examines the role of point interpolation, establishing when different regimes relate to one another and when approximate interpolation can be made exact, and establishes a quantitative relationship between finite-sample point interpolation and distributional approximation for Wasserstein distances.

Antonio 'Alvarez-L'opez, Jing-Pu Cheng, Li-Yan Zhang et al. · 0 citations

#machine learning Preprint Oct 2025

Performance-Efficiency Tradeoffs in Transformers: An Approximation Theory Perspective

This work investigates how the model parameters -- mainly attention heads and head dimensions -- should be allocated across layers to balance expressivity and efficiency, and proposes principled strategies for allocating attention heads and dimensions across Transformers' layers.

Ruoxi Yu, Hao-Tian Jiang, Jingpu Cheng et al. · 1 citation