mlsanityData to 7 Oct 2026

Resourceful Contextual Bandits

arXiv 1402.6779 ↗First posted 27 Feb 2014cs.LG cs.DS cs.GTcited by 45

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Abstract (arXiv)

We study contextual bandits with ancillary constraints on resources, which are common in real-world applications such as choosing ads or dynamic pricing of items. We design the first algorithm for solving these problems that handles constrained resources other than time, and improves over a trivial reduction to the non-contextual case. We consider very general settings for both contextual bandits (arbitrary policy sets, e.g. Dudik et al. (UAI'11)) and bandits with resource constraints (bandits with knapsacks, Badanidiyuru et al. (FOCS'13)), and prove a regret guarantee with near-optimal statistical properties.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Feb 2014.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 5; 2022: 11; 2023: 13; 2024: 3; 2025: 5; 2026: 7.

45 papers.

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  1. 2022Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regressioncited by 30
  2. 2021An Efficient Pessimistic-Optimistic Algorithm for Stochastic Linear Bandits with General Constraintscited by 29
  3. 2021Stochastic Bandits for Multi-platform Budget Optimization in Online Advertisingcited by 25
  4. 2022Safe Linear Bandits over Unknown Polytopescited by 13
  5. 2022Optimal Contextual Bandits with Knapsacks under Realizability via Regression Oraclescited by 12
  6. 2021Learning to be Fair: A Consequentialist Approach to Equitable Decision-Makingcited by 11
  7. 2022Online Contextual Decision-Making with a Smart Predict-then-Optimize Methodcited by 11
  8. 2022No-regret Learning in Repeated First-Price Auctions with Budget Constraintscited by 9
  9. 2023Directional Optimism for Safe Linear Banditscited by 8
  10. 2019Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behaviorcited by 6
  11. 2022Stochastic Conservative Contextual Linear Banditscited by 6
  12. 2023Approximately Stationary Bandits with Knapsackscited by 6
33 more
  1. 2023Bandits with Replenishable Knapsacks: the Best of both Worldscited by 6
  2. 2024No-Regret is not enough! Bandits with General Constraints through Adaptive Regret Minimizationcited by 6
  3. 2023Online Learning under Budget and ROI Constraints via Weak Adaptivitycited by 5
  4. 2025No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need!cited by 5
  5. 2022Best of Many Worlds Guarantees for Online Learning with Knapsackscited by 4
  6. 2023Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgetscited by 4
  7. 2023Price of Safety in Linear Best Arm Identificationcited by 4
  8. 2021A Multi-Arm Bandit Approach To Subset Selection Under Constraintscited by 3
  9. 2022Contextual Bandits with Knapsacks for a Conversion Modelcited by 3
  10. 2022Contextual Decision-Making with Knapsacks Beyond the Worst Casecited by 3
  11. 2023Contextual Bandits with Budgeted Information Revealcited by 3
  12. 2023High-dimensional Linear Bandits with Knapsackscited by 3
  13. 2025Learning to Price with Resource Constraints: From Full Information to Machine-Learned Pricescited by 3
  14. 2025Multiplayer Information Asymmetric Contextual Banditscited by 3
  15. 2022Dynamic Budget Throttling in Repeated Second-Price Auctionscited by 2
  16. 2022Conditionally Risk-Averse Contextual Banditscited by 2
  17. 2023Small Total-Cost Constraints in Contextual Bandits with Knapsacks, with Application to Fairnesscited by 2
  18. 2026WISERouter: LLM Routing with Workload Budget Constraintcited by 2
  19. 2023Online Resource Allocation: Bandits feedback and Advice on Time-varying Demandscited by 1
  20. 2023Risk-Aware Continuous Control with Neural Contextual Banditscited by 1
  21. 2026Non-Stationary Online Resource Allocation: Learning from a Single Samplecited by 1
  22. 2026Cost-Aware Multi-Objective Bandits: Theory and Application to Budgeted LLM Configuration Evaluationcited by 1
  23. 2021Making the most of your day: online learning for optimal allocation of timecited by 0
  24. 2023Clustered Linear Contextual Bandits with Knapsackscited by 0
  25. 2023α-Fair Contextual Banditscited by 0
  26. 2024Budgeted Recommendation with Delayed Feedbackcited by 0
  27. 2024Safe and Efficient Online Convex Optimization with Linear Budget Constraints and Partial Feedbackcited by 0
  28. 2025Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototypecited by 0
  29. 2025Multi-Armed Bandits with Minimum Aggregated Revenue Constraintscited by 0
  30. 2026Combinatorial Allocation Bandits with Nonlinear Arm Utilitycited by 0
  31. 2026A Comparative Analysis on the Performance of Upper Confidence Bound Algorithms in Adaptive Deep Neural Networkscited by 0
  32. 2026Multi-Task Representation Learning for Conservative Linear Banditscited by 0
  33. 2026Reoptimization Algorithms for Contextual Bandits with Knapsack Constraintscited by 0