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Distributional Regression for Arbitrary Response Variables Using ReLU Neural Networks

L5 · ResearcherResearcharXiv· 10/12/2026

Advances theoretical foundations for neural network-based distributional regression with dimensionality-curse overcoming convergence rates.

AI Summary

Researchers propose a unified framework for distributional regression using ReLU neural networks that handles arbitrary response variables with explicit convergence rates.

Excerpt

This paper develops a unified framework for distributional regression with arbitrary response variables. Rather than relying on scalar thresholds or Euclidean structure, we model conditional event probabilities indexed by measurable sets, which extends distributional regression to general response spaces while retaining a direct connection to classical event-based formulations. Within this framework, we study two complementary estimators: a preliminary estimator based on separate regression of e

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