# Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization

arXiv: https://arxiv.org/abs/2205.08295 · HTML: https://mlsanity.com/paper/2205.08295/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 17 May 2022.

## Builds on (most cited first, 10 of 10)

- [Online Clustering of Bandits](https://mlsanity.com/paper/1401.8257/) (2014)
- [Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity](https://mlsanity.com/paper/1909.03539/) (2019)
- [A Gang of Bandits](https://mlsanity.com/paper/1306.0811/) (2013)
- [Improved Algorithm on Online Clustering of Bandits](https://mlsanity.com/paper/1902.09162/) (2019)
- [Action Centered Contextual Bandits](https://mlsanity.com/paper/1711.03596/) (2017)
- [Semiparametric Contextual Bandits](https://mlsanity.com/paper/1803.04204/) (2018)
- [Laplacian-regularized graph bandits: Algorithms and theoretical analysis](https://mlsanity.com/paper/1907.05632/) (2019)
- [Unifying Clustered and Non-stationary Bandits](https://mlsanity.com/paper/2009.02463/) (2020)
- [Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model](https://mlsanity.com/paper/1901.11221/) (2019)
- [Horde of Bandits using Gaussian Markov Random Fields](https://mlsanity.com/paper/1703.02626/) (2017)

## Built on by (most cited first, 4 of 4)

- [Experimental Design for Semiparametric Bandits](https://mlsanity.com/paper/2506.13390/) (2025)
- [A Component-Based Survey of Interactions between Large Language Models and Multi-Armed Bandits](https://mlsanity.com/paper/2601.12945/) (2026)
- [Zero-Inflated Bandits](https://mlsanity.com/paper/2312.15595/) (2023)
- [Nearly Optimal Best Arm Identification for Semiparametric Bandits](https://mlsanity.com/paper/2604.03969/) (2026)

## Abstract (arXiv)

Non-stationarity is ubiquitous in human behavior and addressing it in the contextual bandits is challenging. Several works have addressed the problem by investigating semi-parametric contextual bandits and warned that ignoring non-stationarity could harm performances. Another prevalent human behavior is social interaction which has become available in a form of a social network or graph structure. As a result, graph-based contextual bandits have received much attention. In this paper, we propose "SemiGraphTS," a novel contextual Thompson-sampling algorithm for a graph-based semi-parametric reward model. Our algorithm is the first to be proposed in this setting. We derive an upper bound of the cumulative regret that can be expressed as a multiple of a factor depending on the graph structure and the order for the semi-parametric model without a graph. We evaluate the proposed and existing algorithms via simulation and real data example.