{"ID":2840036,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.14455","arxiv_id":"2511.14455","title":"Nonparametric estimation of conditional probability distributions using a generative approach based on conditional push-forward neural networks","abstract":"We introduce conditional push-forward neural networks (CPFN), a generative framework for conditional distribution estimation. Instead of directly modeling the conditional density $f_{Y|X}$, CPFN learns a stochastic map $\\varphi=\\varphi(x,u)$ such that $\\varphi(x,U)$ and $Y|X=x$ follow approximately the same law, with $U$ a suitable random vector of pre-defined latent variables. This enables efficient conditional sampling and straightforward estimation of conditional statistics through Monte Carlo methods. The model is trained via an objective function derived from a Kullback-Leibler formulation, without requiring invertibility or adversarial training. We establish a near-asymptotic consistency result and demonstrate experimentally that CPFN can achieve performance competitive with, or even superior to, state-of-the-art methods, including kernel estimators, tree-based algorithms, and popular deep learning techniques, all while remaining lightweight and easy to train.","short_abstract":"We introduce conditional push-forward neural networks (CPFN), a generative framework for conditional distribution estimation. Instead of directly modeling the conditional density $f_{Y|X}$, CPFN learns a stochastic map $\\varphi=\\varphi(x,u)$ such that $\\varphi(x,U)$ and $Y|X=x$ follow approximately the same law, with $...","url_abs":"https://arxiv.org/abs/2511.14455","url_pdf":"https://arxiv.org/pdf/2511.14455v3","authors":"[\"Nicola Rares Franco\",\"Lorenzo Tedesco\"]","published":"2025-11-18T12:59:20Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"stat.ME\"]","methods":"[]","has_code":false}