# Online Learning in Contextual Bandits using Gated Linear Networks

arXiv: https://arxiv.org/abs/2002.11611 · HTML: https://mlsanity.com/paper/2002.11611/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [Decentralized Contextual Bandits with Network Adaptivity](https://mlsanity.com/paper/2508.13411/) (2025)

## Abstract (arXiv)

We introduce a new and completely online contextual bandit algorithm called Gated Linear Contextual Bandits (GLCB). This algorithm is based on Gated Linear Networks (GLNs), a recently introduced deep learning architecture with properties well-suited to the online setting. Leveraging data-dependent gating properties of the GLN we are able to estimate prediction uncertainty with effectively zero algorithmic overhead. We empirically evaluate GLCB compared to 9 state-of-the-art algorithms that leverage deep neural networks, on a standard benchmark suite of discrete and continuous contextual bandit problems. GLCB obtains median first-place despite being the only online method, and we further support these results with a theoretical study of its convergence properties.