Robust logistic regression and classification

Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan

Research output: Contribution to journalConference articlepeer-review


We consider logistic regression with arbitrary outliers in the covariate matrix. We propose a new robust logistic regression algorithm, called RoLR, that estimates the parameter through a simple linear programming procedure. We prove that RoLR is robust to a constant fraction of adversarial outliers. To the best of our knowledge, this is the first result on estimating logistic regression model when the covariate matrix is corrupted with any performance guarantees. Besides regression, we apply RoLR to solving binary classification problems where a fraction of training samples are corrupted.

Original languageEnglish
Pages (from-to)253-261
Number of pages9
JournalAdvances in Neural Information Processing Systems
Issue numberJanuary
StatePublished - 2014
Event28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014 - Montreal, Canada
Duration: 8 Dec 201413 Dec 2014

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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