Develops error-propagation bounds and stability estimates for probability-flow ODE distillation, yielding a stability-balanced non-uniform time discretization that improves few-step sampling accuracy.
arXiv preprint arXiv:2601.19285 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
In the oracle continuous-time setting, stochastic interpolation models recover training samples exactly, with deviations controlled by discretization and estimation errors, leading to theoretical definitions of overfitting and underfitting.
citing papers explorer
-
A Quantitative Approximation Framework for Flow Distillation in Diffusion Models
Develops error-propagation bounds and stability estimates for probability-flow ODE distillation, yielding a stability-balanced non-uniform time discretization that improves few-step sampling accuracy.
-
A Theoretical Analysis of Memory and Overfitting Phenomena in Stochastic Interpolation Models
In the oracle continuous-time setting, stochastic interpolation models recover training samples exactly, with deviations controlled by discretization and estimation errors, leading to theoretical definitions of overfitting and underfitting.