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.

中文翻译：

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在线社交学习中的适应

这项工作研究了非平稳条件下的社会学习。尽管专为在线推理而设计，但经典的社交学习算法在漂移条件下表现不佳。为了减轻这个缺点，我们提出了自适应社交学习（ASL）策略。该策略利用自适应贝叶斯更新，其中可以通过调整合适的步长参数来调节适应度。ASL 算法的学习性能通过稳态分析来检验。结果表明，在小步长机制下：i) 一致学习是可能的；ii) 可以根据高斯近似提供对性能的准确预测。