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Distributionally Robust Bayesian Optimization

arXiv 2002.09038 ↗First posted 20 Feb 2020stat.ML cs.LGcited by 60

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Abstract (arXiv)

Robustness to distributional shift is one of the key challenges of contemporary machine learning. Attaining such robustness is the goal of distributionally robust optimization, which seeks a solution to an optimization problem that is worst-case robust under a specified distributional shift of an uncontrolled covariate. In this paper, we study such a problem when the distributional shift is measured via the maximum mean discrepancy (MMD). For the setting of zeroth-order, noisy optimization, we present a novel distributionally robust Bayesian optimization algorithm (DRBO). Our algorithm provably obtains sub-linear robust regret in various settings that differ in how the uncertain covariate is observed. We demonstrate the robust performance of our method on both synthetic and real-world benchmarks.

Key facts. Cited by 60 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 12; 2023: 10; 2024: 4; 2025: 11; 2026: 10.

60 papers.

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  1. 2022Recent Advances in Bayesian Optimizationcited by 74
  2. 2021Misspecified Gaussian Process Bandit Optimizationcited by 52
  3. 2021Unsupervised Learning of Debiased Representations with Pseudo-Attributescited by 44
  4. 2021Neural Contextual Bandits without Regretcited by 40
  5. 2022Robust Multi-Objective Bayesian Optimization Under Input Noisecited by 36
  6. 2023Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?cited by 35
  7. 2022Data-Driven Offline Decision-Making via Invariant Representation Learningcited by 31
  8. 2022Thinking inside the box: A tutorial on grey-box Bayesian optimizationcited by 26
  9. 2021Automatic Termination for Hyperparameter Optimizationcited by 21
  10. 2021Value-at-Risk Optimization with Gaussian Processescited by 19
  11. 2021Risk-averse Heteroscedastic Bayesian Optimizationcited by 17
  12. 2023Distributionally Robust Model-based Reinforcement Learning with Large State Spacescited by 17
38 more
  1. 2022Distributionally Robust Bayesian Optimization with varphi-divergencescited by 16
  2. 2022Near-Optimal Algorithms for Group Distributionally Robust Optimization and Beyondcited by 16
  3. 2021Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learningcited by 14
  4. 2021Bias-Robust Bayesian Optimization via Dueling Banditscited by 11
  5. 2022A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Banditscited by 11
  6. 2021Active learning for distributionally robust level-set estimationcited by 10
  7. 2021Generalization Bounds with Minimal Dependency on Hypothesis Class via Distributionally Robust Optimizationcited by 9
  8. 2021Robust Bandit Learning with Imperfect Contextcited by 8
  9. 2023Designing Fiduciary Artificial Intelligencecited by 8
  10. 2022Movement Penalized Bayesian Optimization with Application to Wind Energy Systemscited by 7
  11. 2022Near-optimal Policy Identification in Active Reinforcement Learningcited by 7
  12. 2021Robust Generalization despite Distribution Shift via Minimum Discriminating Informationcited by 6
  13. 2022Expert-Calibrated Learning for Online Optimization with Switching Costscited by 6
  14. 2022Bayesian Optimization for Distributionally Robust Chance-constrained Problemcited by 5
  15. 2023Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertaintycited by 5
  16. 2025BONSAI: Structure-exploiting robust Bayesian optimization for networked black-box systems under uncertaintycited by 5
  17. 2021Distributionally Robust Optimization with Markovian Datacited by 4
  18. 2023Adversarial Causal Bayesian Optimizationcited by 4
  19. 2024Robust Entropy Search for Safe Efficient Bayesian Optimizationcited by 4
  20. 2025Distributionally Robust Optimization via Diffusion Ambiguity Modelingcited by 4
  21. 2022Mind the Gap: Measuring Generalization Performance Across Multiple Objectivescited by 3
  22. 2023Robust Bayesian Target Value Optimizationcited by 3
  23. 2023Robust Bayesian Satisficingcited by 3
  24. 2023Stochastic Bayesian Optimization with Unknown Continuous Context Distribution via Kernel Density Estimationcited by 3
  25. 2024Active Learning for Level Set Estimation Using Randomized Straddle Algorithmscited by 3
  26. 2025BILBO: BILevel Bayesian Optimizationcited by 3
  27. 2025Performance Comparisons of Reinforcement Learning Algorithms for Sequential Experimental Designcited by 3
  28. 2025Wasserstein Distributionally Robust Bayesian Optimization with Continuous Contextcited by 3
  29. 2026Distributionally Robust Optimization via Generative Ambiguity Modelingcited by 3
  30. 2024Regret Minimization via Saddle Point Optimizationcited by 2
  31. 2025Robust Satisficing Gaussian Process Bandits Under Adversarial Attackscited by 1
  32. 2025Thompson Sampling in Function Spaces via Neural Operatorscited by 1
  33. 2025DRO-InstructZero: Distributionally Robust Prompt Optimization for Large Language Modelscited by 1
  34. 2025Bayesian Ambiguity Contraction-based Adaptive Robust Markov Decision Processes for Adversarial Surveillance Missionscited by 1
  35. 2026Nearly-Optimal Algorithm for Adversarial Kernelized Banditscited by 1
  36. 2026Active Learning for Stochastic Contextual Linear Banditscited by 1
  37. 2023Learning Relevant Contextual Variables Within Bayesian Optimizationcited by 0
  38. 2023Partial identification of kernel based two sample tests with mismeasured datacited by 0

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