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arXiv Statistics ML By Lorenzo Tedesco (University of Bergamo)

Agentic Distributional Regression: Conditional Density Estimation through Agent-Written Programs

Read the original on arXiv Statistics ML →

The paper introduces Agentic Distributional Regression (ADR), a method that uses a language‑model agent to generate a short program estimating conditional densities from iid data. The agent constructs the estimator from a library of probability distributions, producing a parametric, mixture, or multivariate factorization whose parameters are explicit functions of covariates. The authors provide finite‑sample error bounds, an oracle inequality for model selection, and show asymptotic normality of fitted constants, with simulation results indicating ADR outperforms other methods on twelve real datasets and a new post‑training dataset.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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