mlsanityData to 7 Oct 2026

Balanced Linear Contextual Bandits

arXiv 1812.06227 ↗First posted 15 Dec 2018cs.LG stat.MLcited by 30

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Abstract (arXiv)

Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning. We develop algorithms for contextual bandits with linear payoffs that integrate balancing methods from the causal inference literature in their estimation to make it less prone to problems of estimation bias. We provide the first regret bound analyses for linear contextual bandits with balancing and show that our algorithms match the state of the art theoretical guarantees. We demonstrate the strong practical advantage of balanced contextual bandits on a large number of supervised learning datasets and on a synthetic example that simulates model misspecification and prejudice in the initial training data.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Dec 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 7; 2023: 4; 2024: 4; 2025: 6; 2026: 2.

30 papers.

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  1. 2021Local Clustering in Contextual Multi-Armed Banditscited by 29
  2. 2021Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisitingcited by 27
  3. 2021Doubly robust Thompson sampling for linear payoffscited by 21
  4. 2021Online Multi-Armed Bandits with Adaptive Inferencecited by 21
  5. 2021Multi-facet Contextual Bandits: A Neural Network Perspectivecited by 20
  6. 2022Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Banditscited by 14
  7. 2024Online Experimental Design With Estimation-Regret Trade-off Under Network Interferencecited by 9
  8. 2023An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Banditscited by 8
  9. 2024Learning treatment effects while treating those in needcited by 8
  10. 2022Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventionscited by 6
  11. 2022Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Banditscited by 6
  12. 2022Breaking the √T Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Banditscited by 5
18 more
  1. 2022Reward Imputation with Sketching for Contextual Batched Banditscited by 5
  2. 2021Machine Learning for Online Algorithm Selection under Censored Feedbackcited by 3
  3. 2023A Doubly Robust Approach to Sparse Reinforcement Learningcited by 3
  4. 2025Linear Bandits with Partially Observable Featurescited by 3
  5. 2023Improved Algorithms for Multi-period Multi-class Packing Problems with Bandit Feedbackcited by 2
  6. 2022Entropy Regularization for Population Estimationcited by 1
  7. 2024IBCB: Efficient Inverse Batched Contextual Bandit for Behavioral Evolution Historycited by 1
  8. 2025LNUCB-TA: Linear-nonlinear Hybrid Bandit Learning with Temporal Attentioncited by 1
  9. 2025High-dimensional Nonparametric Contextual Bandit Problemcited by 1
  10. 2025Design-Based Bandits Under Network Interference: Trade-Off Between Regret and Statistical Inferencecited by 1
  11. 2021Counterfactual Contextual Multi-Armed Bandit: a Real-World Application to Diagnose Apple Diseasescited by 0
  12. 2022Selectively Contextual Banditscited by 0
  13. 2023Kernel ε-Greedy for Multi-Armed Bandits with Covariatescited by 0
  14. 2024Balancing the Scales: Reinforcement Learning for Fair Classificationcited by 0
  15. 2025Locally Private Nonparametric Contextual Multi-armed Banditscited by 0
  16. 2025Adaptive Data Augmentation for Thompson Samplingcited by 0
  17. 2026Adaptive Exploration for Latent-State Banditscited by 0
  18. 2026Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approachcited by 0