mlsanityData to 7 Oct 2026

Bayesian Optimization for Distributionally Robust Chance-constrained Problem

arXiv 2201.13112 ↗First posted 31 Jan 2022stat.ML cs.LGcited by 5

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Abstract (arXiv)

In black-box function optimization, we need to consider not only controllable design variables but also uncontrollable stochastic environment variables. In such cases, it is necessary to solve the optimization problem by taking into account the uncertainty of the environmental variables. Chance-constrained (CC) problem, the problem of maximizing the expected value under a certain level of constraint satisfaction probability, is one of the practically important problems in the presence of environmental variables. In this study, we consider distributionally robust CC (DRCC) problem and propose a novel DRCC Bayesian optimization method for the case where the distribution of the environmental variables cannot be precisely specified. We show that the proposed method can find an arbitrary accurate solution with high probability in a finite number of trials, and confirm the usefulness of the proposed method through numerical experiments.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 16 of its 36 references that are in the map. First posted on arXiv 31 Jan 2022.

Builds on

16 of 36 references.

References · 16

  1. 2018Stagewise Safe Bayesian Optimization with Gaussian Processescited by 79
  2. 2018Information Directed Sampling and Bandits with Heteroscedastic Noisecited by 75
  3. 2016Safe Exploration in Finite Markov Decision Processes with Gaussian Processescited by 72
  4. 2018Adversarially Robust Optimization with Gaussian Processescited by 61
  5. 2020Distributionally Robust Bayesian Optimizationcited by 60
  6. 2015A General Framework for Constrained Bayesian Optimization using Information-based Searchcited by 50
  7. 2021Value-at-Risk Optimization with Gaussian Processescited by 19
  8. 2018On Distributionally Robust Chance Constrained Programs with Wasserstein Distancecited by 15
  9. 2018Robust Super-Level Set Estimation using Gaussian Processescited by 15
  10. 2020Distributionally Robust Bayesian Quadrature Optimizationcited by 15
  11. 2019Bayesian Experimental Design for Finding Reliable Level Set under Input Uncertaintycited by 10
  12. 2021Active learning for distributionally robust level-set estimationcited by 10
4 more
  1. 2019Active learning for level set estimation under input uncertainty and its extensionscited by 7
  2. 2020Bayesian Quadrature Optimization for Probability Threshold Robustness Measurecited by 5
  3. 2020Distributionally Robust Chance-Constrained Programs with Right-Hand Side Uncertainty under Wasserstein Ambiguitycited by 4
  4. 2021A sampling criterion for constrained Bayesian optimization with uncertaintiescited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 1; 2025: 0; 2026: 1.

5 papers.

  1. 2023Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Boundscited by 16
  2. 2023Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertaintycited by 5
  3. 2024Regret Analysis for Randomized Gaussian Process Upper Confidence Boundcited by 4
  4. 2023Stochastic Bayesian Optimization with Unknown Continuous Context Distribution via Kernel Density Estimationcited by 3
  5. 2026Distributionally Robust Set Representation Learning Under Inference-Time Element Corruptioncited by 0