mlsanityData to 7 Oct 2026

Stochastic Linear Contextual Bandits with Diverse Contexts

arXiv 2003.02681 ↗First posted 5 Mar 2020cs.LG cs.IT math.ITcited by 16

Not on the map. Why?

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.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Mar 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 2; 2023: 2; 2024: 0; 2025: 3; 2026: 7.

16 papers.

Sort:

By area:

  1. 2021Robust Stochastic Linear Contextual Bandits Under Adversarial Attackscited by 33
  2. 2021Leveraging Good Representations in Linear Contextual Banditscited by 29
  3. 2022Langevin Monte Carlo for Contextual Banditscited by 22
  4. 2023VITS : Variational Inference Thompson Sampling for contextual banditscited by 5
  5. 2026Provably Efficient Regularized Online RLHF with Generalized Bilinear Preferencescited by 5
  6. 2025Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contextscited by 3
  7. 2025Federated Linear Dueling Banditscited by 2
  8. 2026Provable and Practical In-Context Policy Optimization for Self-Improvementcited by 1
  9. 2022Truncated LinUCB for Stochastic Linear Banditscited by 0
  10. 2023Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oraclecited by 0
  11. 2025Sparse Nonparametric Contextual Banditscited by 0
  12. 2026Mixing Makes Markovian Contexts Cheap for Linear Banditscited by 0
4 more
  1. 2026Logistic Bandits with tildeO(√dT) Regret without Context Diversity Assumptionscited by 0
  2. 2026Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approachcited by 0
  3. 2026Fast A/B/n Testing: Exact Multi-Policy Comparison via Tree-Coupled Feedback Sharingcited by 0
  4. 2026Minimax Optimal Regret for Causal Logistic Bandits with Counterfactual Fairnesscited by 0