English(EN) SADA: Safe and Adaptive Aggregation of Multiple Black-Box Predictions in Semi-Supervised Learning

新的SADA方法通过聚合黑盒模型改进半监督学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-29 04:00

研究人员开发了SADA，一种在半监督学习场景中安全自适应地聚合多个黑盒模型预测的新方法。该方法保证性能不差于仅使用标记数据，并且如果任何单个预测是完美的，则可以实现最佳效率。该方法已通过模拟和真实数据分析得到证明，并提供了一个配套的R包用于实现。 AI

影响通过实现对不同模型预测的更鲁棒的聚合，增强了半监督学习。

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

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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arXiv stat.ML TIER_1 English(EN) · Jiawei Shan, Zhifeng Chen, Yiming Dong, Yazhen Wang, Jiwei Zhao · 2026-05-29 04:00

SADA：半监督学习中多个黑盒预测的安全自适应聚合

arXiv:2509.21707v3 Announce Type: replace Abstract: Semi-supervised learning (SSL) arises in practice when labeled data are scarce or expensive to obtain, while large quantities of unlabeled data are readily available. With the growing adoption of machine learning techniques, it …