mlsanityData to 14 Oct 2026

Risk-averse Heteroscedastic Bayesian Optimization

arXiv 2111.03637 ↗First posted 5 Nov 2021cs.LGcited by 17

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Abstract (arXiv)

Many black-box optimization tasks arising in high-stakes applications require risk-averse decisions. The standard Bayesian optimization (BO) paradigm, however, optimizes the expected value only. We generalize BO to trade mean and input-dependent variance of the objective, both of which we assume to be unknown a priori. In particular, we propose a novel risk-averse heteroscedastic Bayesian optimization algorithm (RAHBO) that aims to identify a solution with high return and low noise variance, while learning the noise distribution on the fly. To this end, we model both expectation and variance as (unknown) RKHS functions, and propose a novel risk-aware acquisition function. We bound the regret for our approach and provide a robust rule to report the final decision point for applications where only a single solution must be identified. We demonstrate the effectiveness of RAHBO on synthetic benchmark functions and hyperparameter tuning tasks.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 21 of its 34 references that are in the map. First posted on arXiv 5 Nov 2021.

Builds on

21 of 34 references.

References · 21

  1. 2017On Kernelized Multi-armed Banditscited by 352
  2. 2016Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Roboticscited by 134
  3. 2019ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendationscited by 77
  4. 2018Information Directed Sampling and Bandits with Heteroscedastic Noisecited by 76
  5. 2019Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspacescited by 73
  6. 2018Adversarially Robust Optimization with Gaussian Processescited by 62
  7. 2020Distributionally Robust Bayesian Optimizationcited by 61
  8. 2018Bayesian Optimization in AlphaGocited by 55
  9. 2020BOSS: Bayesian Optimization over String Spacescited by 54
  10. 2016Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimationcited by 40
  11. 2016Practical heteroskedastic Gaussian process modeling for large simulation experimentscited by 39
  12. 2020Bayesian Optimization of Risk Measurescited by 36
9 more
  1. 2020Corruption-Tolerant Gaussian Process Bandit Optimizationcited by 34
  2. 2016Automatic LQR Tuning Based on Gaussian Process Global Optimizationcited by 30
  3. 2017Replication or exploration? Sequential design for stochastic simulation experimentscited by 30
  4. 2015Bayesian Optimization for Synthetic Gene Designcited by 21
  5. 2019Bayesian Optimization under Heavy-tailed Payoffscited by 21
  6. 2021Value-at-Risk Optimization with Gaussian Processescited by 20
  7. 2020Mean-Variance Analysis in Bayesian Optimization under Uncertaintycited by 18
  8. 2020Distributionally Robust Bayesian Quadrature Optimizationcited by 15
  9. 2020Mixed Strategies for Robust Optimization of Unknown Objectivescited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 7; 2024: 3; 2025: 3; 2026: 1.

17 papers.

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  1. 2022Bayesian Optimization with Conformal Prediction Setscited by 28
  2. 2022A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Banditscited by 11
  3. 2023Likelihood Ratio Confidence Sets for Sequential Decision Makingcited by 10
  4. 2023Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimationcited by 8
  5. 2023Batch Bayesian Optimization for Replicable Experimental Designcited by 8
  6. 2023Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertaintycited by 5
  7. 2023Adversarial Causal Bayesian Optimizationcited by 4
  8. 2023Constrained Bayesian Optimization with Adaptive Active Learning of Unknown Constraintscited by 2
  9. 2024Batched Energy-Entropy acquisition for Bayesian Optimizationcited by 2
  10. 2025Safe Bayesian optimization across noise models via scenario programmingcited by 1
  11. 2022Learning Disturbances Online for Risk-Aware Control: Risk-Aware Flight with Less Than One Minute of Datacited by 0
  12. 2023Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holescited by 0
5 more
  1. 2024A Comparative Study of Hyperparameter Tuning Methodscited by 0
  2. 2024Theoretical Analysis of Heteroscedastic Gaussian Processes with Posterior Distributionscited by 0
  3. 2025Why risk matters for protein binder designcited by 0
  4. 2025No-Regret Gaussian Process Optimization of Time-Varying Functionscited by 0
  5. 2026Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRLcited by 0