arXiv stat.ML
· Papers
Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust
arXiv:2608.12470v1 Announce Type: cross Abstract: Large astrophysical simulation campaigns often generate training data by sampling parameters across a Uniform prior box. Due to the proposal's sharp edge, neural posterior estimators struggle to learn accurate approximations near the boundaries. We propose Tailed-Unifor