8 papers
PROVE: Training-Free Prompt Recovery using Verifiable Evidence
Rupayan Mallick, Mahsa Khoshnoodi, Sarah Adel Bargal
Modern text-to-image models can generate highly realistic images from natural-language prompts, while recent advances in prompt inversion have made it increasingly feasible to reco…
Nonparametric Estimation under General Nonlinear ODE Constraints: A Comparison with Parametric ODE-Fitting Methods
Chunlei Ge, W. John Braun
Many physical, biological, and epidemiological processes are governed by ordinary differential equations (ODEs) that are nonlinear in the state variable, including logistic populat…
Local Quasi-Linear Models: Kernel Differential Equation Regression and Fire Data Analysis
Chunlei Ge, W. John Braun
We introduce the local quasi-linear (LQL) model, a differential equation-constrained local polynomial regression framework for the general first-order linear ordinary differential…
Differential Equation-Constrained Exponential-Type Local Polynomial Regression Under Model Misspecification
Chunlei Ge, W. John Braun
The issue of model misspecification is critical, yet it is often regarded as unavoidable in applied statistical modeling. Model misspecification can be mitigated by incorporating i…
Differential Equation-Constrained Local Regression for Data with Sparse Design
Chunlei Ge, W. John Braun
Local polynomial regression of order one or higher often performs poorly in areas with sparse data. In contrast, local constant regression tends to be more robust in these regions,…
On Data Sharpening in Nonparametric Autoregressive Models
Simon Snyman, Lengyi Han, W. John Braun
Data sharpening has been shown to reduce bias in nonparametric regression and density estimation. Its performance on nonlinear first order autoregressive models is studied theoreti…