Error Analysis of Neural-Network-Based Engression
Juntong Chen, Zijian Guo, Xinwei Shen
stat.ML cs.LG stat.ME
Abstract
Engression (Shen and Meinshausen, 2024) learns a conditional distribution by fitting a generative model $Y = f(X,\varepsilon)$ under the energy score, a strictly proper scoring rule. We provide a theoretical error analysis of engression implemented with deep neural networks. We decompose the excess risk into three components: the approximation error, the stochastic error, and the Monte Carlo error. Based on this decomposition, we establish convergence rates under the assumption that the target conditional generator admits a compositional smoothness structure.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.