# Local Uncertainty Sampling for Large-Scale Multi-Class Logistic Regression

arXiv: https://arxiv.org/abs/1604.08098 · HTML: https://mlsanity.com/paper/1604.08098/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2016.

## Built on by (most cited first, 5 of 5)

- [Gaussian Switch Sampling: A Second Order Approach to Active Learning](https://mlsanity.com/paper/2302.12018/) (2023)
- [Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data](https://mlsanity.com/paper/2110.13048/) (2021)
- [Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning](https://mlsanity.com/paper/2309.02476/) (2023)
- [Optimizing Active Learning for Low Annotation Budgets](https://mlsanity.com/paper/2201.07200/) (2022)
- [Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems](https://mlsanity.com/paper/2305.16391/) (2023)

## Abstract (arXiv)

A major challenge for building statistical models in the big data era is that the available data volume far exceeds the computational capability. A common approach for solving this problem is to employ a subsampled dataset that can be handled by available computational resources. In this paper, we propose a general subsampling scheme for large-scale multi-class logistic regression and examine the variance of the resulting estimator. We show that asymptotically, the proposed method always achieves a smaller variance than that of the uniform random sampling. Moreover, when the classes are conditionally imbalanced, significant improvement over uniform sampling can be achieved. Empirical performance of the proposed method is compared to other methods on both simulated and real-world datasets, and these results match and confirm our theoretical analysis.