arXiv：cs.LG（机器学习，全量分类）· Xinwei Shen, Zijian Guo, Francis Bach·· 15 小时前AI 评分35
广义 Engression 模型：统一任意类型结果的非参数分布回归框架
Generalized Engression Models
AI 导读
研究者提出广义 Engression 模型，一个面向连续、二值、分类、有序及排序等任意类型结果的统一非参数分布回归框架，通过数据类型专属链接函数与随机扰动平滑损失，实现含不连续链接时的梯度训练。该方法在 242 物种群落生态基准和 17 维混合类型健康结果两项应用中，边际得分与类型专属模型持平，联合分布表现更优，并匹配或超越专用 SOTA 联合物种分布模型。软件以 Python 提供。
正文
Abstract:We consider estimating the conditional distribution of a multivariate outcome given covariates when its coordinates may be continuous, binary, categorical, ordinal or rankings, and are conditionally dependent on one another. Different statistical methods have been developed for each outcome type, and most of them target a summary of the conditional distribution, such as the mean of each coordinate, rather than the joint distribution of the outcome vector. We develop generalized engression models, a unified nonparametric distributional regression framework for outcomes of any type. The proposed method builds upon engression, a scoring-rule-based deep generative model, and introduces a data-type-specific link function and a stochastic perturbation that smooths the loss, enabling gradient-based training even with discontinuous links. We establish universal representation results for continuous, discrete and mixed outcomes. In simulations and in two applications, 242 species in a community ecology benchmark and a 17-dimensional mixed-type health outcome, the method matches type-specific models on marginal scores, improves on them on the joint distribution, and matches or exceeds purpose-built state-of-the-art joint species distribution models. Software is available in Python.
| Subjects: | Methodology (stat.ME); Machine Learning (cs.LG); Machine Learning (stat.ML) |
| Cite as: | arXiv:2610.01823 [stat.ME] |
| (or arXiv:2610.01823v1 [stat.ME] for this version) | |
| https://doi.org/10.48550/arXiv.2610.01823 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xinwei Shen [view email]
[v1]
Thu, 1 Oct 2026 14:57:35 UTC (603 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org