We present an efficient parameter-free approach for statistical learning from corrupted training sets. We identify corrupted and non-corrupted samples using latent Bernoulli variables, and therefore formulate the robust learning problem as maximization of the likelihood where latent variables are marginalized out. The resulting optimization problem is solved via variational inference using an efficient Expectation-Maximization based method. The proposed approach improves over the state-of-the-art by automatically inferring the corruption level and identifying outliers, while adding minimal computational overhead. We demonstrate our robust learning method on a wide variety of machine learning tasks including online learning and deep learning where it exhibits ability to adapt to different levels of noise and attain high prediction accuracy.

我们提出了一种高效的无参数方法，用于从受损训练集中进行统计学习。我们使用潜在的伯努利变量来确定受损和非受损样本，从而将鲁棒学习问题形式化为最大化似然函数，在其中对潜在变量进行边缘化处理。我们使用基于期望最大化的高效变分推断方法来解决由此产生的优化问题。所提出的方法通过自动推断损坏程度和识别异常值，同时添加最小的计算开销，胜过现有技术水平。我们在各种机器学习任务上展示了我们的鲁棒学习方法，包括在线学习和深度学习，在这些任务中，它表现出适应不同噪声水平和高预测准确性的能力。

基于潜在伯努利变量的自适应无参数鲁棒学习