mlsanityData to 14 Oct 2026

Practical Contextual Bandits with Feedback Graphs

arXiv 2302.08631 ↗First posted 17 Feb 2023cs.LGcited by 11

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Abstract (arXiv)

While contextual bandit has a mature theory, effectively leveraging different feedback patterns to enhance the pace of learning remains unclear. Bandits with feedback graphs, which interpolates between the full information and bandit regimes, provides a promising framework to mitigate the statistical complexity of learning. In this paper, we propose and analyze an approach to contextual bandits with feedback graphs based upon reduction to regression. The resulting algorithms are computationally practical and achieve established minimax rates, thereby reducing the statistical complexity in real-world applications.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 24 of its 37 references that are in the map. First posted on arXiv 17 Feb 2023.

Builds on

24 of 37 references.

References · 24

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2021The Statistical Complexity of Interactive Decision Makingcited by 218
  3. 2014Taming the Monster: A Fast and Simple Algorithm for Contextual Banditscited by 201
  4. 2020Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oraclescited by 197
  5. 2015Explore no more: Improved high-probability regret bounds for non-stochastic banditscited by 120
  6. 2020Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizabilitycited by 115
  7. 2018Practical Contextual Bandits with Regression Oraclescited by 93
  8. 2021Adapting to Misspecification in Contextual Banditscited by 81
  9. 2015Online Learning with Feedback Graphs: Beyond Banditscited by 80
  10. 2014Nonstochastic Multi-Armed Bandits with Graph-Structured Feedbackcited by 61
  11. 2021Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discriminationcited by 53
  12. 2016The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Banditscited by 49
12 more
  1. 2013From Bandits to Experts: A Tale of Domination and Independencecited by 28
  2. 2016Online Learning with Feedback Graphs Without the Graphscited by 27
  3. 2022Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphscited by 26
  4. 2020A Closer Look at Small-loss Bounds for Bandits with Graph Feedbackcited by 21
  5. 2017Small-loss bounds for online learning with partial informationcited by 18
  6. 2022Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spacescited by 18
  7. 2021Beyond Bandit Feedback in Online Multiclass Classificationcited by 14
  8. 2019Stochastic Online Learning with Probabilistic Graph Feedbackcited by 12
  9. 2022Learning on the Edge: Online Learning with Stochastic Feedback Graphscited by 12
  10. 2020Contextual Bandits with Side-Observationscited by 5
  11. 2022Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphscited by 5
  12. 2020Adversarial Linear Contextual Bandits with Graph-Structured Side Observationscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 2; 2026: 4.

11 papers.

  1. 2024Efficient Contextual Bandits with Uninformed Feedback Graphscited by 6
  2. 2025Single Index Bandits: Generalized Linear Contextual Bandits with Unknown Reward Functionscited by 6
  3. 2024Stochastic contextual bandits with graph feedback: from independence number to MAS numbercited by 5
  4. 2024Contextual Multinomial Logit Bandits with General Value Functionscited by 4
  5. 2025Pure Exploration with Feedback Graphscited by 4
  6. 2024Provably Efficient Interactive-Grounded Learning with Personalized Rewardcited by 3
  7. 2026Correlation-Aware Contextual Bandits with Surrogate Rewards for LLM Routingcited by 3
  8. 2023Stochastic Graph Bandit Learning with Side-Observationscited by 1
  9. 2026Laplacian Kernelized Banditcited by 1
  10. 2026Interaction-Grounded Learning for Contextual Markov Decision Processes with Personalized Feedbackcited by 0
  11. 2026Efficient contextual partial monitoring with general function approximationcited by 0