Robust Linear Regression Against Training Data Poisoning
Robust Linear Regression Against Training Data Poisoning
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DOI：
10.1145/3128572.3140447
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Chang Liu;Bo Li;Yevgeniy Vorobeychik;Alina Oprea
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文献类型：
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作者：
Chang Liu;Bo Li;Yevgeniy Vorobeychik;Alina Oprea
监督学习技术的有效性使其在研究和实践中无处不在。在高维环境中，监督学习通常依赖于降维来提高性能，并确定预测结果的最重要因素。然而，学习的经济重要性使其成为训练数据对抗性操纵的自然目标，我们称之为中毒攻击。先前处理鲁棒监督学习的方法依赖于对特征矩阵性质的强假设，例如特征独立性和低方差的亚高斯噪声。我们提出了一种集成的鲁棒回归方法，该方法放宽了这些假设，仅假设特征矩阵可以很好地由低秩矩阵近似。我们的技术集成了改进的鲁棒低秩矩阵近似和鲁棒主成分回归，并产生了强大的性能保证。此外，我们通过实验表明，我们的方法在运行时间和预测误差方面都明显优于现有的方法。
The effectiveness of supervised learning techniques has made them ubiquitous in research and practice. In high-dimensional settings, supervised learning commonly relies on dimensionality reduction to improve performance and identify the most important factors in predicting outcomes. However, the economic importance of learning has made it a natural target for adversarial manipulation of training data, which we term poisoning attacks. Prior approaches to dealing with robust supervised learning rely on strong assumptions about the nature of the feature matrix, such as feature independence and sub-Gaussian noise with low variance. We propose an integrated method for robust regression that relaxes these assumptions, assuming only that the feature matrix can be well approximated by a low-rank matrix. Our techniques integrate improved robust low-rank matrix approximation and robust principle component regression, and yield strong performance guarantees. Moreover, we experimentally show that our methods significantly outperform state of the art both in running time and prediction error.