# Robust online active learning

arXiv: https://arxiv.org/abs/2302.00422 · HTML: https://mlsanity.com/paper/2302.00422/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 53 references that are in the map.
- First posted on arXiv 1 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Towards Robust Interpretability with Self-Explaining Neural Networks](https://mlsanity.com/paper/1806.07538/) (2018)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [On Statistical Bias In Active Learning: How and When To Fix It](https://mlsanity.com/paper/2101.11665/) (2021)
- [Latent Outlier Exposure for Anomaly Detection with Contaminated Data](https://mlsanity.com/paper/2202.08088/) (2022)
- [GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection](https://mlsanity.com/paper/1903.06661/) (2019)
- [A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping](https://mlsanity.com/paper/1409.5165/) (2014)
- [Stopping criterion for active learning based on deterministic generalization bounds](https://mlsanity.com/paper/2005.07402/) (2020)
- [Hitting the Target: Stopping Active Learning at the Cost-Based Optimum](https://mlsanity.com/paper/2110.03802/) (2021)
- [Stream-based active learning with linear models](https://mlsanity.com/paper/2207.09874/) (2022)
- [Active Learning for Accurate Estimation of Linear Models](https://mlsanity.com/paper/1703.00579/) (2017)
- [Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders](https://mlsanity.com/paper/2212.13067/) (2022)
- [Online Active Linear Regression via Thresholding](https://mlsanity.com/paper/1602.02845/) (2016)
- [A sub-sampling algorithm preventing outliers](https://mlsanity.com/paper/2208.06218/) (2022)

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

- [Active learning for data streams: a survey](https://mlsanity.com/paper/2302.08893/) (2023)
- [Robust Sampling for Active Statistical Inference](https://mlsanity.com/paper/2511.08991/) (2025)
- [Camera clustering for scalable stream-based active distillation](https://mlsanity.com/paper/2404.10411/) (2024)
- [Data-driven jet fuel demand forecasting: A case study of Copenhagen Airport](https://mlsanity.com/paper/2511.05569/) (2025)
- [Implicit Multi-Camera System Calibration Using Gaussian Processes](https://mlsanity.com/paper/2605.07491/) (2026)

## Abstract (arXiv)

In many industrial applications, obtaining labeled observations is not straightforward as it often requires the intervention of human experts or the use of expensive testing equipment. In these circumstances, active learning can be highly beneficial in suggesting the most informative data points to be used when fitting a model. Reducing the number of observations needed for model development alleviates both the computational burden required for training and the operational expenses related to labeling. Online active learning, in particular, is useful in high-volume production processes where the decision about the acquisition of the label for a data point needs to be taken within an extremely short time frame. However, despite the recent efforts to develop online active learning strategies, the behavior of these methods in the presence of outliers has not been thoroughly examined. In this work, we investigate the performance of online active linear regression in contaminated data streams. Our study shows that the currently available query strategies are prone to sample outliers, whose inclusion in the training set eventually degrades the predictive performance of the models. To address this issue, we propose a solution that bounds the search area of a conditional D-optimal algorithm and uses a robust estimator. Our approach strikes a balance between exploring unseen regions of the input space and protecting against outliers. Through numerical simulations, we show that the proposed method is effective in improving the performance of online active learning in the presence of outliers, thus expanding the potential applications of this powerful tool.