论文标题
在线社会学习中的适应
Adaptation in Online Social Learning
论文作者
论文摘要
这项工作研究了非平稳条件下的社会学习。尽管为在线推断设计，但经典的社会学习算法在漂流条件下的表现较差。为了减轻这种缺点，我们提出了自适应社会学习（ASL）策略。该策略利用自适应的贝叶斯更新，可以通过调整合适的步进参数来调制自适应度。通过稳态分析检查ASL算法的学习性能。结果表明，在小步骤的制度下：i）一致的学习是可能的； ii）可以根据高斯近似来提供对性能的准确预测。
This work studies social learning under non-stationary conditions. Although designed for online inference, classic social learning algorithms perform poorly under drifting conditions. To mitigate this drawback, we propose the Adaptive Social Learning (ASL) strategy. This strategy leverages an adaptive Bayesian update, where the adaptation degree can be modulated by tuning a suitable step-size parameter. The learning performance of the ASL algorithm is examined by means of a steady-state analysis. It is shown that, under the regime of small step-sizes: i) consistent learning is possible; ii) an accurate prediction of the performance can be furnished in terms of a Gaussian approximation.
