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Doubly-Robust Lasso Bandit

arXiv 1907.11362 ↗First posted 26 Jul 2019stat.ML cs.LGcited by 49

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Abstract (arXiv)

Contextual multi-armed bandit algorithms are widely used in sequential decision tasks such as news article recommendation systems, web page ad placement algorithms, and mobile health. Most of the existing algorithms have regret proportional to a polynomial function of the context dimension, d. In many applications however, it is often the case that contexts are high-dimensional with only a sparse subset of size s0 (ll d) being correlated with the reward. We consider the stochastic linear contextual bandit problem and propose a novel algorithm, namely the Doubly-Robust Lasso Bandit algorithm, which exploits the sparse structure of the regression parameter as in Lasso, while blending the doubly-robust technique used in missing data literature. The high-probability upper bound of the regret incurred by the proposed algorithm does not depend on the number of arms and scales with log(d) instead of a polynomial function of d. The proposed algorithm shows good performance when contexts of different arms are correlated and requires less tuning parameters than existing methods.

Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Jul 2019.

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Citing papers per year: 2021: 5; 2022: 15; 2023: 12; 2024: 5; 2025: 8; 2026: 4.

49 papers.

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  1. 2021Neural Contextual Bandits without Regretcited by 40
  2. 2021Doubly robust Thompson sampling for linear payoffscited by 21
  3. 2021A Simple Unified Framework for High Dimensional Bandit Problemscited by 21
  4. 2021Information Directed Sampling for Sparse Linear Banditscited by 20
  5. 2022Multi-task Representation Learning with Stochastic Linear Banditscited by 20
  6. 2022PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Banditscited by 17
  7. 2022Contextual Information-Directed Samplingcited by 16
  8. 2022Thompson Sampling for High-Dimensional Sparse Linear Contextual Banditscited by 16
  9. 2022Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Banditscited by 14
  10. 2022Variance-Aware Sparse Linear Banditscited by 11
  11. 2021Regret Lower Bound and Optimal Algorithm for High-Dimensional Contextual Linear Banditcited by 8
  12. 2023Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricingcited by 8
37 more
  1. 2023Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneitycited by 7
  2. 2022Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Banditscited by 6
  3. 2023Anytime Model Selection in Linear Banditscited by 6
  4. 2025Single Index Bandits: Generalized Linear Contextual Bandits with Unknown Reward Functionscited by 6
  5. 2022Meta-Learning Hypothesis Spaces for Sequential Decision-makingcited by 5
  6. 2022Reward Imputation with Sketching for Contextual Batched Banditscited by 5
  7. 2023High-dimensional Contextual Bandit Problem without Sparsitycited by 5
  8. 2026Provably Efficient Regularized Online RLHF with Generalized Bilinear Preferencescited by 5
  9. 2022Federated Online Sparse Decision Makingcited by 3
  10. 2022Lifelong Bandit Optimization: No Prior and No Regretcited by 3
  11. 2023Does Sparsity Help in Learning Misspecified Linear Bandits?cited by 3
  12. 2023A Doubly Robust Approach to Sparse Reinforcement Learningcited by 3
  13. 2023High-dimensional Linear Bandits with Knapsackscited by 3
  14. 2025Linear Bandits with Partially Observable Featurescited by 3
  15. 2026Valid Best-Model Identification for LLM Evaluation via Low-Rank Factorizationcited by 3
  16. 2022High dimensional stochastic linear contextual bandit with missing covariatescited by 2
  17. 2023Improved Algorithms for Multi-period Multi-class Packing Problems with Bandit Feedbackcited by 2
  18. 2024Low-Rank Online Dynamic Assortment with Dual Contextual Informationcited by 2
  19. 2022Advertising Media and Target Audience Optimization via High-dimensional Banditscited by 1
  20. 2023Stochastic Contextual Bandits with Long Horizon Rewardscited by 1
  21. 2023Fixed-Budget Best-Arm Identification in Sparse Linear Banditscited by 1
  22. 2023New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problemcited by 1
  23. 2024Symmetric Linear Bandits with Hidden Symmetrycited by 1
  24. 2024FLIPHAT: Joint Differential Privacy for High Dimensional Sparse Linear Banditscited by 1
  25. 2025A Unified Regularization Approach to High-Dimensional Generalized Tensor Banditscited by 1
  26. 2025High-dimensional Nonparametric Contextual Bandit Problemcited by 1
  27. 2025Sparse Optimistic Information Directed Samplingcited by 1
  28. 2022Bayesian Non-stationary Linear Bandits for Large-Scale Recommender Systemscited by 0
  29. 2022On Penalization in Stochastic Multi-armed Banditscited by 0
  30. 2023Cooperative Thresholded Lasso for Sparse Linear Banditcited by 0
  31. 2024Batched Online Contextual Sparse Bandits with Sequential Inclusion of Featurescited by 0
  32. 2024Sparse Linear Bandits with Blocking Constraintscited by 0
  33. 2025Sparse Nonparametric Contextual Banditscited by 0
  34. 2025Adaptive Data Augmentation for Thompson Samplingcited by 0
  35. 2025Navigating Sparsities in High-Dimensional Linear Contextual Banditscited by 0
  36. 2026Learning to Sparsify Stochastic Linear Banditscited by 0
  37. 2026Learning to target with network interferencecited by 0