TY - GEN
T1 - Robust linear regression against training data poisoning
AU - Liu, Chang
AU - Li, Bo
AU - Vorobeychik, Yevgeniy
AU - Oprea, Alina
PY - 2017/11/3
Y1 - 2017/11/3
N2 - 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 robust regression both in running time and prediction error.
AB - 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 robust regression both in running time and prediction error.
UR - http://www.scopus.com/inward/record.url?scp=85037366729&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85037366729&partnerID=8YFLogxK
U2 - 10.1145/3128572.3140447
DO - 10.1145/3128572.3140447
M3 - Conference contribution
AN - SCOPUS:85037366729
T3 - AISec 2017 - Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, co-located with CCS 2017
SP - 91
EP - 102
BT - AISec 2017 - Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, co-located with CCS 2017
PB - Association for Computing Machinery, Inc
T2 - 10th ACM Workshop on Artificial Intelligence and Security, AISec 2017
Y2 - 3 November 2017
ER -