# Stochastic Linear Contextual Bandits with Diverse Contexts

arXiv: https://arxiv.org/abs/2003.02681 · HTML: https://mlsanity.com/paper/2003.02681/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 16 of 16)

- [Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks](https://mlsanity.com/paper/2106.02978/) (2021)
- [Leveraging Good Representations in Linear Contextual Bandits](https://mlsanity.com/paper/2104.03781/) (2021)
- [Langevin Monte Carlo for Contextual Bandits](https://mlsanity.com/paper/2206.11254/) (2022)
- [VITS : Variational Inference Thompson Sampling for contextual bandits](https://mlsanity.com/paper/2307.10167/) (2023)
- [Provably Efficient Regularized Online RLHF with Generalized Bilinear Preferences](https://mlsanity.com/paper/2602.23116/) (2026)
- [Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contexts](https://mlsanity.com/paper/2501.00891/) (2025)
- [Federated Linear Dueling Bandits](https://mlsanity.com/paper/2502.01085/) (2025)
- [Provable and Practical In-Context Policy Optimization for Self-Improvement](https://mlsanity.com/paper/2603.01335/) (2026)
- [Truncated LinUCB for Stochastic Linear Bandits](https://mlsanity.com/paper/2202.11735/) (2022)
- [Logistic Bandits with tildeO(√dT) Regret without Context Diversity Assumptions](https://mlsanity.com/paper/2604.22161/) (2026)
- [Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle](https://mlsanity.com/paper/2301.12571/) (2023)
- [Sparse Nonparametric Contextual Bandits](https://mlsanity.com/paper/2503.16382/) (2025)
- [Mixing Makes Markovian Contexts Cheap for Linear Bandits](https://mlsanity.com/paper/2603.12530/) (2026)
- [Set Membership Estimation for Stochastic Linear Contextual Bandits](https://mlsanity.com/paper/2606.20022/) (2026)
- [Fast A/B/n Testing: Exact Multi-Policy Comparison via Tree-Coupled Feedback Sharing](https://mlsanity.com/paper/2608.12831/) (2026)
- [Minimax Optimal Regret for Causal Logistic Bandits with Counterfactual Fairness](https://mlsanity.com/paper/2610.01377/) (2026)

## Abstract (arXiv)

In this paper, we investigate the impact of context diversity on stochastic linear contextual bandits. As opposed to the previous view that contexts lead to more difficult bandit learning, we show that when the contexts are sufficiently diverse, the learner is able to utilize the information obtained during exploitation to shorten the exploration process, thus achieving reduced regret. We design the LinUCB-d algorithm, and propose a novel approach to analyze its regret performance. The main theoretical result is that under the diverse context assumption, the cumulative expected regret of LinUCB-d is bounded by a constant. As a by-product, our results improve the previous understanding of LinUCB and strengthen its performance guarantee.