# OLR-WA Online Regression with Weighted Average

arXiv: https://arxiv.org/abs/2307.02804 · HTML: https://mlsanity.com/paper/2307.02804/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 6 Jul 2023.

## Builds on (most cited first, 3 of 3)

- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Lecture notes on ridge regression](https://mlsanity.com/paper/1509.09169/) (2015)
- [Online Learning to Sample](https://mlsanity.com/paper/1506.09016/) (2015)

## Built on by (most cited first, 2 of 2)

- [OLR-WA: Online Weighted Average Linear Regression in Multivariate Data Streams](https://mlsanity.com/paper/2512.14892/) (2025)
- [Online Performance Estimation with Unlabeled Data: A Bayesian Application of the Hui-Walter Paradigm](https://mlsanity.com/paper/2401.09376/) (2024)

## Abstract (arXiv)

Machine Learning requires a large amount of training data in order to build accurate models. Sometimes the data arrives over time, requiring significant storage space and recalculating the model to account for the new data. On-line learning addresses these issues by incrementally modifying the model as data is encountered, and then discarding the data. In this study we introduce a new online linear regression approach. Our approach combines newly arriving data with a previously existing model to create a new model. The introduced model, named OLR-WA (OnLine Regression with Weighted Average) uses user-defined weights to provide flexibility in the face of changing data to bias the results in favor of old or new data. We have conducted 2-D and 3-D experiments comparing OLR-WA to a static model using the entire data set. The results show that for consistent data, OLR-WA and the static batch model perform similarly and for varying data, the user can set the OLR-WA to adapt more quickly or to resist change.