mlsanityData to 7 Oct 2026

Multi-Task Learning for Contextual Bandits

arXiv 1705.08618 ↗First posted 24 May 2017stat.ML cs.LGcited by 47

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Abstract (arXiv)

Contextual bandits are a form of multi-armed bandit in which the agent has access to predictive side information (known as the context) for each arm at each time step, and have been used to model personalized news recommendation, ad placement, and other applications. In this work, we propose a multi-task learning framework for contextual bandit problems. Like multi-task learning in the batch setting, the goal is to leverage similarities in contexts for different arms so as to improve the agent's ability to predict rewards from contexts. We propose an upper confidence bound-based multi-task learning algorithm for contextual bandits, establish a corresponding regret bound, and interpret this bound to quantify the advantages of learning in the presence of high task (arm) similarity. We also describe an effective scheme for estimating task similarity from data, and demonstrate our algorithm's performance on several data sets.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 May 2017.

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Citing papers per year: 2021: 14; 2022: 14; 2023: 6; 2024: 6; 2025: 6; 2026: 1.

47 papers.

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  1. 2021Adaptive Methods for Real-World Domain Generalizationcited by 66
  2. 2021Meta-Thompson Samplingcited by 62
  3. 2021Hierarchical Bayesian Banditscited by 33
  4. 2021No Regrets for Learning the Prior in Banditscited by 29
  5. 2022Provable Benefit of Multitask Representation Learning in Reinforcement Learningcited by 29
  6. 2021Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximationcited by 23
  7. 2022Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelinescited by 22
  8. 2021Multi-facet Contextual Bandits: A Neural Network Perspectivecited by 20
  9. 2023Graph Neural Banditscited by 20
  10. 2022Nearly Minimax Algorithms for Linear Bandits with Shared Representationcited by 16
  11. 2022Mixed-Effect Thompson Samplingcited by 14
  12. 2021Collaborative Pure Exploration in Kernel Banditcited by 13
35 more
  1. 2022Non-Stationary Representation Learning in Sequential Linear Banditscited by 13
  2. 2021Bandit Algorithms for Precision Medicinecited by 12
  3. 2023Online Clustering of Bandits with Misspecified User Modelscited by 12
  4. 2021Multitask Learning and Bandits via Robust Statisticscited by 11
  5. 2022Meta-Learning for Simple Regret Minimizationcited by 10
  6. 2023Neural Contextual Bandits for Personalized Recommendationcited by 10
  7. 2022Multi-Task Off-Policy Learning from Bandit Feedbackcited by 9
  8. 2021Robust Bandit Learning with Imperfect Contextcited by 8
  9. 2022Neural Bandit with Arm Group Graphcited by 8
  10. 2022Neural Collaborative Filtering Bandits via Meta Learningcited by 7
  11. 2023Evaluating Online Bandit Exploration In Large-Scale Recommender Systemcited by 7
  12. 2023Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneitycited by 7
  13. 2021Adaptive Methods for Aggregated Domain Generalizationcited by 6
  14. 2024Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Banditscited by 6
  15. 2022On the Sample Complexity of Representation Learning in Multi-task Bandits with Global and Local structurecited by 5
  16. 2024Diffusion Models Meet Contextual Banditscited by 5
  17. 2022Meta Learning MDPs with Linear Transition Modelscited by 4
  18. 2022Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanismscited by 4
  19. 2022Multi-Environment Meta-Learning in Stochastic Linear Banditscited by 4
  20. 2025Transfer Learning in Latent Contextual Bandits with Covariate Shift Through Causal Transportabilitycited by 4
  21. 2024Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraintscited by 2
  22. 2024Transfer in Sequential Multi-armed Bandits via Reward Samplescited by 2
  23. 2025MINT: Multimodal Instruction Tuning with Multimodal Interaction Groupingcited by 2
  24. 2022PRIMA: Planner-Reasoner Inside a Multi-task Reasoning Agentcited by 1
  25. 2024Oralytics Reinforcement Learning Algorithmcited by 1
  26. 2024Exploiting Adjacent Similarity in Multi-Armed Bandit Tasks via Transfer of Reward Samplescited by 1
  27. 2026Scaling Reasoning Tokens via RL and Parallel Thinking: Evidence From Competitive Programmingcited by 1
  28. 2021Bandits with Stochastic Experts: Constant Regret, Empirical Experts and Episodescited by 0
  29. 2021Convolutional Neural Bandit for Visual-aware Recommendationcited by 0
  30. 2021Recommending with Recommendationscited by 0
  31. 2023Adaptive Interventions with User-Defined Goals for Health Behavior Changecited by 0
  32. 2025Locally Private Nonparametric Contextual Multi-armed Banditscited by 0
  33. 2025Representative Action Selection for Large Action Space Bandit Familiescited by 0
  34. 2025Empirical Bayesian Multi-Bandit Learningcited by 0
  35. 2025Co-Exploration and Co-Exploitation via Shared Structure in Multi-Task Banditscited by 0