mlsanityData to 7 Oct 2026

Efficient Contextual Bandits with Continuous Actions

arXiv 2006.06040 ↗First posted 10 Jun 2020cs.LG stat.MLcited by 24

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Abstract (arXiv)

We create a computationally tractable algorithm for contextual bandits with continuous actions having unknown structure. Our reduction-style algorithm composes with most supervised learning representations. We prove that it works in a general sense and verify the new functionality with large-scale experiments.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jun 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 4; 2023: 6; 2024: 4; 2025: 3; 2026: 3.

24 papers.

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  1. 2021Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networkscited by 52
  2. 2022Contextual Bandits with Large Action Spaces: Made Practicalcited by 32
  3. 2021Lipschitz Bandits with Batched Feedbackcited by 21
  4. 2022Deep Hierarchy in Banditscited by 18
  5. 2022Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spacescited by 17
  6. 2023Oracle-Efficient Pessimism: Offline Policy Optimization in Contextual Banditscited by 14
  7. 2021Top-k eXtreme Contextual Bandits with Arm Hierarchycited by 8
  8. 2024Probabilities of Causation for Continuous and Vector Variablescited by 8
  9. 2023Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimizationcited by 7
  10. 2024Information Capacity Regret Bounds for Bandits with Mediator Feedbackcited by 4
  11. 2021Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimizationcited by 3
  12. 2023Optimal Exploration is no harder than Thompson Samplingcited by 3
12 more
  1. 2024DISCO: An End-to-End Bandit Framework for Personalised Discount Allocationcited by 3
  2. 2023Learning to Generate All Feasible Actionscited by 2
  3. 2023Infinite Action Contextual Bandits with Reusable Data Exhaustcited by 2
  4. 2024Batched Stochastic Bandit for Nondegenerate Functionscited by 2
  5. 2026Taming the Monster Every Context: Complexity Measure and Unified Framework for Offline-Oracle Efficient Contextual Banditscited by 2
  6. 2022Reducing Learning Difficulties: One-Step Two-Critic Deep Reinforcement Learning for Inverter-based Volt-Var Controlcited by 0
  7. 2023Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instancescited by 0
  8. 2025Learn to Bid as a Price-Maker Wind Power Producercited by 0
  9. 2025A General Approach of Automated Environment Design for Learning the Optimal Power Flowcited by 0
  10. 2025Batched Bandits with Heavy-Tailed Rewardscited by 0
  11. 2026Online Adaptation of Visual Odometry Frontends with Image-Conditioned Reinforcement Learningcited by 0
  12. 2026Safety by Design: Realized-Cost Constraints for Contextual Bandits with Continuous Actionscited by 0