English(EN) The Fenchel-Young Generalization of Bayesian Principles

新的 Fenchel-Young 推广增强了机器学习中的贝叶斯原理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了一个广义的机器学习贝叶斯原理框架，扩展了 Fenchel-Young 不等式。这种新方法允许计算上更简单的后验更新，并在等式处定义广义后验。该框架还引入了一种新的共轭概念，支持加性正则更新、参数依赖支持和可调重尾，这些是标准共轭贝叶斯所不具备的功能。 AI

影响这项理论进展可能导致机器学习应用中更强大、更灵活的贝叶斯模型。

排序理由该集群包含一篇详细介绍机器学习新理论框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Sophia Sklaviadis, Thomas Moellenhoff, Emtiyaz Khan, Andre Martins, Mario Figueiredo · 2026-10-23 04:00

The Fenchel-Young Generalization of Bayesian Principles

arXiv:2502.10295v4 Announce Type: replace Abstract: Conjugate Bayes plays a key role in machine learning, ensuring computationally simple posterior updates. We generalize the variational principle behind this computation by using the Fenchel-Young (FY) inequality, which lets us d…