Robust linear regression against training data poisoning

Chang Liu, Bo Li, Yevgeniy Vorobeychik, Alina Oprea

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationAISec 2017 - Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, co-located with CCS 2017
PublisherAssociation for Computing Machinery, Inc
Pages91-102
Number of pages12
ISBN (Electronic)9781450352024
DOIs
StatePublished - Nov 3 2017
Externally publishedYes
Event10th ACM Workshop on Artificial Intelligence and Security, AISec 2017 - Dallas, United States
Duration: Nov 3 2017 → …

Publication series

NameAISec 2017 - Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, co-located with CCS 2017

Other

Other10th ACM Workshop on Artificial Intelligence and Security, AISec 2017
CountryUnited States
CityDallas
Period11/3/17 → …

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Liu, C., Li, B., Vorobeychik, Y., & Oprea, A. (2017). Robust linear regression against training data poisoning. In AISec 2017 - Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, co-located with CCS 2017 (pp. 91-102). (AISec 2017 - Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, co-located with CCS 2017). Association for Computing Machinery, Inc. https://doi.org/10.1145/3128572.3140447