English(EN) Agentic Distributional Regression: Conditional Density Estimation through Agent-Written Programs

AI代理编写程序进行密度估计，性能优于现有方法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员推出了一种名为Agentic Distributional Regression (ADR) 的新方法，该方法使用语言模型代理来估计条件密度。这些代理编写程序，为每个协变量值输出估计的条件分布，并从概率分布库中提取。ADR在模拟研究和真实世界数据集上均表现出优越的性能，甚至在利用Claude Opus 5.5时，其性能优于在代理训练期后收集的数据上训练的方法。 AI

影响这项研究可能带来更复杂的AI驱动的统计建模和分析工具。

排序理由该集群描述了一篇学术论文中提出的一种新方法论。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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arXiv stat.ML TIER_1 English(EN) · Lorenzo Tedesco (University of Bergamo) · 2026-10-28 04:00

Agentic Distributional Regression: 通过Agent编写程序进行条件密度估计

arXiv:2610.37611v1 Announce Type: cross Abstract: In this paper we propose a method for estimating conditional densities from an iid sample using a language-model agent. The agent writes the estimator as a short program that, for each covariate value, returns the estimated condit…

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Agentic Distributional Regression: 通过Agent编写程序进行条件密度估计

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