mlsanityData to 7 Oct 2026

Risk-Averse Stochastic Convex Bandit

arXiv 1810.00737 ↗First posted 1 Oct 2018cs.LG stat.MLcited by 13

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Abstract (arXiv)

Motivated by applications in clinical trials and finance, we study the problem of online convex optimization (with bandit feedback) where the decision maker is risk-averse. We provide two algorithms to solve this problem. The first one is a descent-type algorithm which is easy to implement. The second algorithm, which combines the ellipsoid method and a center point device, achieves (almost) optimal regret bounds with respect to the number of rounds. To the best of our knowledge this is the first attempt to address risk-aversion in the online convex bandit problem.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Oct 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 3; 2023: 1; 2024: 2; 2025: 2; 2026: 1.

13 papers.

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  1. 2021Risk-Aware Learning for Scalable Voltage Optimization in Distribution Gridscited by 5
  2. 2021ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicinecited by 5
  3. 2022Risk-Averse No-Regret Learning in Online Convex Gamescited by 5
  4. 2023A Model-Based Method for Minimizing CVaR and Beyondcited by 5
  5. 2021A Survey of Learning Criteria Going Beyond the Usual Riskcited by 4
  6. 2022A Zeroth-Order Momentum Method for Risk-Averse Online Convex Gamescited by 3
  7. 2022Conditionally Risk-Averse Contextual Banditscited by 2
  8. 2024Risk-averse Learning with Non-Stationary Distributionscited by 2
  9. 2025Risk-Averse Constrained Reinforcement Learning with Optimized Certainty Equivalentscited by 1
  10. 2026OrderGrad: Optimizing Beyond the Mean with Order-Statistic Policy Gradient Estimationcited by 1
  11. 2021Uncertainty Principles in Risk-Aware Statistical Estimationcited by 0
  12. 2024Risk-averse learning with delayed feedbackcited by 0
1 more
  1. 2025Risk-Averse Learning with Varying Risk Levelscited by 0