# Resourceful Contextual Bandits

arXiv: https://arxiv.org/abs/1402.6779 · HTML: https://mlsanity.com/paper/1402.6779/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2014.

## Built on by (most cited first, 30 of 45)

- [Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression](https://mlsanity.com/paper/2211.07484/) (2022)
- [An Efficient Pessimistic-Optimistic Algorithm for Stochastic Linear Bandits with General Constraints](https://mlsanity.com/paper/2102.05295/) (2021)
- [Stochastic Bandits for Multi-platform Budget Optimization in Online Advertising](https://mlsanity.com/paper/2103.10246/) (2021)
- [Safe Linear Bandits over Unknown Polytopes](https://mlsanity.com/paper/2209.13694/) (2022)
- [Online Contextual Decision-Making with a Smart Predict-then-Optimize Method](https://mlsanity.com/paper/2206.07316/) (2022)
- [Optimal Contextual Bandits with Knapsacks under Realizability via Regression Oracles](https://mlsanity.com/paper/2210.11834/) (2022)
- [Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making](https://mlsanity.com/paper/2109.08792/) (2021)
- [No-regret Learning in Repeated First-Price Auctions with Budget Constraints](https://mlsanity.com/paper/2205.14572/) (2022)
- [Directional Optimism for Safe Linear Bandits](https://mlsanity.com/paper/2308.15006/) (2023)
- [Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior](https://mlsanity.com/paper/1910.11703/) (2019)
- [Stochastic Conservative Contextual Linear Bandits](https://mlsanity.com/paper/2203.15629/) (2022)
- [Approximately Stationary Bandits with Knapsacks](https://mlsanity.com/paper/2302.14686/) (2023)
- [Bandits with Replenishable Knapsacks: the Best of both Worlds](https://mlsanity.com/paper/2306.08470/) (2023)
- [No-Regret is not enough! Bandits with General Constraints through Adaptive Regret Minimization](https://mlsanity.com/paper/2405.06575/) (2024)
- [Online Learning under Budget and ROI Constraints via Weak Adaptivity](https://mlsanity.com/paper/2302.01203/) (2023)
- [No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need!](https://mlsanity.com/paper/2506.13244/) (2025)
- [Best of Many Worlds Guarantees for Online Learning with Knapsacks](https://mlsanity.com/paper/2202.13710/) (2022)
- [Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets](https://mlsanity.com/paper/2306.07709/) (2023)
- [Price of Safety in Linear Best Arm Identification](https://mlsanity.com/paper/2309.08709/) (2023)
- [A Multi-Arm Bandit Approach To Subset Selection Under Constraints](https://mlsanity.com/paper/2102.04824/) (2021)
- [Contextual Bandits with Knapsacks for a Conversion Model](https://mlsanity.com/paper/2206.00314/) (2022)
- [Contextual Decision-Making with Knapsacks Beyond the Worst Case](https://mlsanity.com/paper/2211.13952/) (2022)
- [Contextual Bandits with Budgeted Information Reveal](https://mlsanity.com/paper/2305.18511/) (2023)
- [High-dimensional Linear Bandits with Knapsacks](https://mlsanity.com/paper/2311.01327/) (2023)
- [Learning to Price with Resource Constraints: From Full Information to Machine-Learned Prices](https://mlsanity.com/paper/2501.14155/) (2025)
- [Multiplayer Information Asymmetric Contextual Bandits](https://mlsanity.com/paper/2503.08961/) (2025)
- [WISERouter: LLM Routing with Workload Budget Constraint](https://mlsanity.com/paper/2607.23765/) (2026)
- [Cost-Aware Multi-Objective Bandits: Theory and Application to Budgeted LLM Configuration Evaluation](https://mlsanity.com/paper/2608.04333/) (2026)
- [Dynamic Budget Throttling in Repeated Second-Price Auctions](https://mlsanity.com/paper/2207.04690/) (2022)
- [Conditionally Risk-Averse Contextual Bandits](https://mlsanity.com/paper/2210.13573/) (2022)

## 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.