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Variational Bayesian Optimal Experimental Design

arXiv 1903.05480 ↗First posted 13 Mar 2019stat.ML cs.LG stat.COcited by 116

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Abstract (arXiv)

Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources. Unfortunately, its applicability is hampered by the difficulty of obtaining accurate estimates of the expected information gain (EIG) of an experiment. To address this, we introduce several classes of fast EIG estimators by building on ideas from amortized variational inference. We show theoretically and empirically that these estimators can provide significant gains in speed and accuracy over previous approaches. We further demonstrate the practicality of our approach on a number of end-to-end experiments.

Key facts. Cited by 116 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Mar 2019.

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Citing papers per year: 2021: 21; 2022: 18; 2023: 16; 2024: 18; 2025: 18; 2026: 25.

116 papers.

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  1. 2023Modern Bayesian Experimental Designcited by 121
  2. 2021Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Designcited by 103
  3. 2023GFlowNets for AI-Driven Scientific Discoverycited by 63
  4. 2021Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoodscited by 61
  5. 2023Prediction-Oriented Bayesian Active Learningcited by 60
  6. 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
  7. 2021OpenBox: A Generalized Black-box Optimization Servicecited by 46
  8. 2022Optimizing Sequential Experimental Design with Deep Reinforcement Learningcited by 45
  9. 2021Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Informationcited by 33
  10. 2024Bayesian Preference Elicitation with Language Modelscited by 32
  11. 2021Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Boundscited by 26
  12. 2021Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outlierscited by 22
38 more
  1. 2021Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learningcited by 22
  2. 2024Amortized Bayesian Experimental Design for Decision-Makingcited by 20
  3. 2021Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimizationcited by 18
  4. 2022Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Modelscited by 15
  5. 2023Differentiable Multi-Target Causal Bayesian Experimental Designcited by 15
  6. 2025BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discoverycited by 15
  7. 2021Active Learning in Bayesian Neural Networks with Balanced Entropy Learning Principlecited by 14
  8. 2024Variational Bayesian Optimal Experimental Design with Normalizing Flowscited by 13
  9. 2025Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systemscited by 13
  10. 2021Targeted Active Learning for Bayesian Decision-Makingcited by 12
  11. 2022Robust Expected Information Gain for Optimal Bayesian Experimental Design Using Ambiguity Setscited by 12
  12. 2022Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attackscited by 12
  13. 2025Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Designcited by 12
  14. 2022Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transportcited by 11
  15. 2023Enhanced Multimodal Representation Learning with Cross-modal KDcited by 11
  16. 2022Scalable First-Order Bayesian Optimization via Structured Automatic Differentiationcited by 10
  17. 2022Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUScited by 10
  18. 2024Probabilistic Bayesian optimal experimental design using conditional normalizing flowscited by 10
  19. 2022Active Exploration for Robotic Manipulationcited by 9
  20. 2023OpenBox: A Python Toolkit for Generalized Black-box Optimizationcited by 9
  21. 2024Bayesian Experimental Design via Contrastive Diffusionscited by 9
  22. 2023Variational Sequential Optimal Experimental Design using Reinforcement Learningcited by 8
  23. 2024Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlocited by 8
  24. 2025ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisitioncited by 8
  25. 2021A Scalable Gradient-Free Method for Bayesian Experimental Design with Implicit Modelscited by 7
  26. 2021Deep Bayesian Active Learning for Accelerating Stochastic Simulationcited by 7
  27. 2022TransBO: Hyperparameter Optimization via Two-Phase Transfer Learningcited by 7
  28. 2022Design Amortization for Bayesian Optimal Experimental Designcited by 7
  29. 2024Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitionscited by 7
  30. 2021Explore the Context: Optimal Data Collection for Context-Conditional Dynamics Modelscited by 6
  31. 2022Statistical applications of contrastive learningcited by 5
  32. 2023Statistically Efficient Bayesian Sequential Experiment Design via Reinforcement Learning with Cross-Entropy Estimatorscited by 5
  33. 2023On Estimating the Gradient of the Expected Information Gain in Bayesian Experimental Designcited by 5
  34. 2023Bayesian Active Learning in the Presence of Nuisance Parameterscited by 5
  35. 2024PASOA- PArticle baSed Bayesian Optimal Adaptive designcited by 5
  36. 2024Challenges and Considerations in the Evaluation of Bayesian Causal Discoverycited by 5
  37. 2021A Practical & Unified Notation for Information-Theoretic Quantities in MLcited by 4
  38. 2021Bayesian Optimal Experimental Design for Simulator Models of Cognitioncited by 4

50 most cited of 116 All 116 (JSON)