# Robust Logistic Regression using Shift Parameters (Long Version)

arXiv: https://arxiv.org/abs/1305.4987 · HTML: https://mlsanity.com/paper/1305.4987/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2013.

## Built on by (most cited first, 3 of 3)

- [Distributionally Robust Learning](https://mlsanity.com/paper/2108.08993/) (2021)
- [Characterizing Information Seeking Events in Health-Related Social Discourse](https://mlsanity.com/paper/2308.09156/) (2023)
- [Robust Linear Classification from Limited Training Data](https://mlsanity.com/paper/2110.01648/) (2021)

## Abstract (arXiv)

Annotation errors can significantly hurt classifier performance, yet datasets are only growing noisier with the increased use of Amazon Mechanical Turk and techniques like distant supervision that automatically generate labels. In this paper, we present a robust extension of logistic regression that incorporates the possibility of mislabelling directly into the objective. Our model can be trained through nearly the same means as logistic regression, and retains its efficiency on high-dimensional datasets. Through named entity recognition experiments, we demonstrate that our approach can provide a significant improvement over the standard model when annotation errors are present.