English(EN) Parameter Estimation for Unnormalized Discrete Models via Empirically Localized Deformed Bregman Divergence

新方法改进离散模型参数估计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-28 04:00

研究人员开发了一种新方法，通过结合经验局部化和变形Bregman散度来估计非归一化离散模型中的参数。该方法显著降低了计算归一化常数的计算成本。Bregman散度的变形选择使得提出的估计器具有良好的统计特性，包括效率和对异常值噪声的鲁棒性。 AI

影响提高了离散概率模型参数估计的计算效率和鲁棒性。

排序理由该集群包含一篇详细介绍机器学习新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Takashi Takenouchi · 2026-09-28 04:00

通过经验局部化变形 Bregman 散度对非归一化离散模型进行参数估计

arXiv:2609.30713v1 Announce Type: new Abstract: Estimation of parameter of probabilistic models is an important task in the field of machine learning.For models of discrete variables, calculation of the normalization constant of model is sometimes difficult and a lot of researche…