mlsanityData to 7 Oct 2026

Modern Bayesian Experimental Design

arXiv 2302.14545 ↗First posted 28 Feb 2023stat.ML cs.AI cs.LGcited by 121

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Abstract (arXiv)

Bayesian experimental design (BED) provides a powerful and general framework for optimizing the design of experiments. However, its deployment often poses substantial computational challenges that can undermine its practical use. In this review, we outline how recent advances have transformed our ability to overcome these challenges and thus utilize BED effectively, before discussing some key areas for future development in the field.

Key facts. Cited by 121 papers in the map (data through 7 Oct 2026). Builds on 48 of its 164 references that are in the map. First posted on arXiv 28 Feb 2023.

Builds on

48 of 164 references.

References · 48

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  3. 2016Variational Inference: A Review for Statisticianscited by 1,466
  4. 2017Deep Bayesian Active Learning with Image Datacited by 812
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  8. 2019BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learningcited by 428
  9. 2017Max-value Entropy Search for Efficient Bayesian Optimizationcited by 202
  10. 2018Deep Variational Reinforcement Learning for POMDPscited by 122
  11. 2019Variational Bayesian Optimal Experimental Designcited by 116
  12. 2021Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Designcited by 103
36 more
  1. 2014Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing Itcited by 92
  2. 2018Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Studycited by 90
  3. 2015Predictive Entropy Search for Multi-objective Bayesian Optimizationcited by 88
  4. 2017On Nesting Monte Carlo Estimatorscited by 82
  5. 2021Active Testing: Sample-Efficient Model Evaluationcited by 82
  6. 2016Likelihood-free inference by ratio estimationcited by 80
  7. 2021On Statistical Bias In Active Learning: How and When To Fix Itcited by 72
  8. 2019Bayesian Batch Active Learning as Sparse Subset Approximationcited by 68
  9. 2021Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoodscited by 61
  10. 2019A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experimentscited by 58
  11. 2020Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimationcited by 56
  12. 2022Optimizing Sequential Experimental Design with Deep Reinforcement Learningcited by 45
  13. 2015GLASSES: Relieving The Myopia Of Bayesian Optimisationcited by 38
  14. 2019BINOCULARS for Efficient, Nonmyopic Sequential Experimental Designcited by 35
  15. 2018Efficient Bayesian Experimental Design for Implicit Modelscited by 34
  16. 2021Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Informationcited by 33
  17. 2016Sequential Bayesian optimal experimental design via approximate dynamic programmingcited by 27
  18. 2021Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Boundscited by 26
  19. 2022Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluationcited by 26
  20. 2021Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learningcited by 22
  21. 2020Unbiased MLMC stochastic gradient-based optimization of Bayesian experimental designscited by 19
  22. 2019Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networkscited by 18
  23. 2021Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimizationcited by 18
  24. 2013On Russian Roulette Estimates for Bayesian Inference with Doubly-Intractable Likelihoodscited by 16
  25. 2018Simple Regret Minimization for Contextual Banditscited by 16
  26. 2022Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Modelscited by 15
  27. 2018Multilevel Monte Carlo estimation of expected information gainscited by 13
  28. 2022Robust Expected Information Gain for Optimal Bayesian Experimental Design Using Ambiguity Setscited by 12
  29. 2018Bayesian design of experiments for intractable likelihood models using coupled auxiliary models and multivariate emulationcited by 10
  30. 2020Response-adaptive randomization in clinical trials: from myths to practical considerationscited by 10
  31. 2019Bayesian decision-theoretic design of experiments under an alternative modelcited by 8
  32. 2022Characterizing the robustness of Bayesian adaptive experimental designs to active learning biascited by 8
  33. 2021A Scalable Gradient-Free Method for Bayesian Experimental Design with Implicit Modelscited by 7
  34. 2022Design Amortization for Bayesian Optimal Experimental Designcited by 7
  35. 2016Practical optimal experiment design with probabilistic programscited by 5
  36. 2022Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisationcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 10; 2024: 26; 2025: 42; 2026: 43.

121 papers.

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  1. 2023Prediction-Oriented Bayesian Active Learningcited by 60
  2. 2024Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AIcited by 60
  3. 2024Doing Experiments and Revising Rules with Natural Language and Probabilistic Reasoningcited by 23
  4. 2025BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Designcited by 21
  5. 2024CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-trainingcited by 20
  6. 2024Amortized Bayesian Experimental Design for Decision-Makingcited by 20
  7. 2024(Implicit) Ensembles of Ensembles: Epistemic Uncertainty Collapse in Large Modelscited by 15
  8. 2024On Subjective Uncertainty Quantification and Calibration in Natural Language Generationcited by 14
  9. 2024Variational Bayesian Optimal Experimental Design with Normalizing Flowscited by 13
  10. 2024Nesting Particle Filters for Experimental Design in Dynamical Systemscited by 12
  11. 2025Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Designcited by 12
  12. 2024Making Better Use of Unlabelled Data in Bayesian Active Learningcited by 10
38 more
  1. 2024Amortized Active Causal Induction with Deep Reinforcement Learningcited by 10
  2. 2025Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluationcited by 10
  3. 2024Bayesian Experimental Design via Contrastive Diffusionscited by 9
  4. 2023Variational Sequential Optimal Experimental Design using Reinforcement Learningcited by 8
  5. 2024Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlocited by 8
  6. 2024Automated Discovery of Pairwise Interactions from Unstructured Datacited by 8
  7. 2025What Data Enables Optimal Decisions? An Exact Characterization for Linear Optimizationcited by 8
  8. 2025ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisitioncited by 8
  9. 2023Deep Stochastic Processes via Functional Markov Transition Operatorscited by 7
  10. 2024Active Diffusion Subsamplingcited by 7
  11. 2025Efficient Evaluation of Multi-Task Robot Policies With Active Experiment Selectioncited by 7
  12. 2025PABBO: Preferential Amortized Black-Box Optimizationcited by 7
  13. 2023Statistically Efficient Bayesian Sequential Experiment Design via Reinforcement Learning with Cross-Entropy Estimatorscited by 5
  14. 2023On Estimating the Gradient of the Expected Information Gain in Bayesian Experimental Designcited by 5
  15. 2023Bayesian Active Learning in the Presence of Nuisance Parameterscited by 5
  16. 2023Adaptive Instrument Design for Indirect Experimentscited by 5
  17. 2024PASOA- PArticle baSed Bayesian Optimal Adaptive designcited by 5
  18. 2024CoCoG: Controllable Visual Stimuli Generation based on Human Concept Representationscited by 5
  19. 2025Nested Expectations with Kernel Quadraturecited by 5
  20. 2025The Curious Language Model: Strategic Test-Time Information Acquisitioncited by 5
  21. 2026MADE: Benchmark Environments for Closed-Loop Materials Discoverycited by 5
  22. 2025Position: Biology is the Challenge Physics-Informed ML Needs to Evolvecited by 4
  23. 2023Designing Optimal Behavioral Experiments Using Machine Learningcited by 3
  24. 2023Bayesian Optimisation Against Climate Change: Applications and Benchmarkscited by 3
  25. 2023Data-Induced Interactions of Sparse Sensors Using Statistical Physicscited by 3
  26. 2024Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Designcited by 3
  27. 2025Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies From Simulated Nonparametric Functionscited by 3
  28. 2026ATLAS: Active Theory Learning for Automated Sciencecited by 3
  29. 2024A Likelihood-Free Approach to Goal-Oriented Bayesian Optimal Experimental Designcited by 2
  30. 2024Is merging worth it? Securely evaluating the information gain for causal dataset acquisitioncited by 2
  31. 2024Proxy-informed Bayesian transfer learning with unknown sourcescited by 2
  32. 2025Optimizing Likelihoods via Mutual Information: Bridging Simulation-Based Inference and Bayesian Optimal Experimental Designcited by 2
  33. 2025PIED: Physics-Informed Experimental Design for Inverse Problemscited by 2
  34. 2025Bayesian Experimental Design for Model Discrepancy Calibration: An Auto-Differentiable Ensemble Kalman Inversion Approachcited by 2
  35. 2025On Efficient Bayesian Exploration in Model-Based Reinforcement Learningcited by 2
  36. 2025A Geometric Approach to Optimal Experimental Designcited by 2
  37. 2025Accelerated Learning on Large Scale Screens using Generative Library Modelscited by 2
  38. 2025Informed Initialization for Bayesian Optimization and Active Learningcited by 2

50 most cited of 121 All 121 (JSON)