mlsanityData to 14 Oct 2026

Value-at-Risk Optimization with Gaussian Processes

arXiv 2105.06126 ↗First posted 13 May 2021cs.LGcited by 20

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Abstract (arXiv)

Value-at-risk (VaR) is an established measure to assess risks in critical real-world applications with random environmental factors. This paper presents a novel VaR upper confidence bound (V-UCB) algorithm for maximizing the VaR of a black-box objective function with the first no-regret guarantee. To realize this, we first derive a confidence bound of VaR and then prove the existence of values of the environmental random variable (to be selected to achieve no regret) such that the confidence bound of VaR lies within that of the objective function evaluated at such values. Our V-UCB algorithm empirically demonstrates state-of-the-art performance in optimizing synthetic benchmark functions, a portfolio optimization problem, and a simulated robot task.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 14 of its 23 references that are in the map. First posted on arXiv 13 May 2021.

Builds on

14 of 23 references.

References · 14

  1. 2017On Kernelized Multi-armed Banditscited by 352
  2. 2017Max-value Entropy Search for Efficient Bayesian Optimizationcited by 206
  3. 2018Adversarially Robust Optimization with Gaussian Processescited by 62
  4. 2020Distributionally Robust Bayesian Optimizationcited by 61
  5. 2016Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimationcited by 40
  6. 2020Bayesian Optimization of Risk Measurescited by 36
  7. 2018Bayesian Optimization with Expensive Integrandscited by 24
  8. 2017Decentralized High-Dimensional Bayesian Optimization with Factor Graphscited by 23
  9. 2020Top-k Ranking Bayesian Optimizationcited by 22
  10. 2020Mean-Variance Analysis in Bayesian Optimization under Uncertaintycited by 18
  11. 2020Bayesian Quantile and Expectile Optimisationcited by 17
  12. 2019Bayesian Optimization with Binary Auxiliary Informationcited by 15
2 more
  1. 2020Distributionally Robust Bayesian Quadrature Optimizationcited by 15
  2. 2020Private Outsourced Bayesian Optimizationcited by 15

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 7; 2023: 4; 2024: 1; 2025: 2; 2026: 4.

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  2. 2024Prompt Optimization with Human Feedbackcited by 35
  3. 2021Differentially Private Federated Bayesian Optimization with Distributed Explorationcited by 32
  4. 2022Data-Driven Offline Decision-Making via Invariant Representation Learningcited by 31
  5. 2022Thinking inside the box: A tutorial on grey-box Bayesian optimizationcited by 26
  6. 2022Sample-Then-Optimize Batch Neural Thompson Samplingcited by 25
  7. 2021Risk-averse Heteroscedastic Bayesian Optimizationcited by 17
  8. 2022A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Banditscited by 11
  9. 2022Bayesian Optimization under Stochastic Delayed Feedbackcited by 10
  10. 2023Batch Bayesian Optimization for Replicable Experimental Designcited by 8
  11. 2022Bayesian Optimization for Distributionally Robust Chance-constrained Problemcited by 5
  12. 2023Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertaintycited by 5
8 more
  1. 2023Training-Free Neural Active Learning with Initialization-Robustness Guaranteescited by 5
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  4. 2025Robust Satisficing Gaussian Process Bandits Under Adversarial Attackscited by 1
  5. 2026Nearly-Optimal Algorithm for Adversarial Kernelized Banditscited by 1
  6. 2025CatBOX: A Categorical-Continuous Bayesian Optimization with Spectral Mixture Kernels for Accelerated Catalysis Experimentscited by 0
  7. 2026Maximizing Reliability with Bayesian Optimizationcited by 0
  8. 2026Bayesian Optimization of CVaR with Unknown Hyperparameterscited by 0