English(EN) Anchor-Based Heteroscedastic Noise for Preferential Bayesian Optimization

新的PBO方法使用用户锚点来模拟比较不确定性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-19 04:00

研究人员开发了一种新的偏好贝叶斯优化（PBO）方法，该方法能够考虑偏好学习过程中用户不确定性的变化。这种称为基于锚点的异方差噪声的方法，使用一小组可靠的示例或“锚点”来创建输入相关的用户置信度图。然后将此图集成到高斯过程代理中，以导出平衡效用和比较难易程度的采集函数，从而提高在合成和人类偏好数据集上的性能。 AI

影响在涉及人类参与的场景中，引入了一种新的偏好学习方法，可以提高模型准确性。

排序理由该集群包含一篇详细介绍偏好贝叶斯优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Marshal Arijona Sinaga, Julien Martinelli, Samuel Kaski · 2026-05-19 04:00

基于锚点的异方差噪声用于优先贝叶斯优化

arXiv:2405.14657v2 Announce Type: replace-cross Abstract: Preferential Bayesian optimization (PBO) learns latent utilities from pairwise comparisons, but most existing methods assume homoscedastic comparison noise. This is inadequate in human-in-the-loop settings, where a user ma…

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基于锚点的异方差噪声用于优先贝叶斯优化

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