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A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments

arXiv 1911.00294 ↗First posted 1 Nov 2019stat.ML cs.LG stat.COcited by 58

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Abstract (arXiv)

We introduce a fully stochastic gradient based approach to Bayesian optimal experimental design (BOED). Our approach utilizes variational lower bounds on the expected information gain (EIG) of an experiment that can be simultaneously optimized with respect to both the variational and design parameters. This allows the design process to be carried out through a single unified stochastic gradient ascent procedure, in contrast to existing approaches that typically construct a pointwise EIG estimator, before passing this estimator to a separate optimizer. We provide a number of different variational objectives including the novel adaptive contrastive estimation (ACE) bound. Finally, we show that our gradient-based approaches are able to provide effective design optimization in substantially higher dimensional settings than existing approaches.

Key facts. Cited by 58 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Nov 2019.

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Citing papers per year: 2021: 10; 2022: 9; 2023: 9; 2024: 10; 2025: 10; 2026: 10.

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  1. 2023Modern Bayesian Experimental Designcited by 125
  2. 2021Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Designcited by 106
  3. 2021Active Testing: Sample-Efficient Model Evaluationcited by 82
  4. 2023GFlowNets for AI-Driven Scientific Discoverycited by 63
  5. 2021Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoodscited by 61
  6. 2021Investigating the Role of Negatives in Contrastive Representation Learningcited by 54
  7. 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
  8. 2022Optimizing Sequential Experimental Design with Deep Reinforcement Learningcited by 46
  9. 2021Decomposed Mutual Information Estimation for Contrastive Representation Learningcited by 25
  10. 2024Amortized Bayesian Experimental Design for Decision-Makingcited by 21
  11. 2021Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimizationcited by 18
  12. 2022Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Modelscited by 16
38 more
  1. 2023Differentiable Multi-Target Causal Bayesian Experimental Designcited by 15
  2. 2024Variational Bayesian Optimal Experimental Design with Normalizing Flowscited by 13
  3. 2021Targeted Active Learning for Bayesian Decision-Makingcited by 12
  4. 2022Robust Expected Information Gain for Optimal Bayesian Experimental Design Using Ambiguity Setscited by 12
  5. 2025Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Designcited by 12
  6. 2024Bayesian Experimental Design via Contrastive Diffusionscited by 11
  7. 2024Probabilistic Bayesian optimal experimental design using conditional normalizing flowscited by 10
  8. 2024Amortized Active Causal Induction with Deep Reinforcement Learningcited by 10
  9. 2023Variational Sequential Optimal Experimental Design using Reinforcement Learningcited by 8
  10. 2025ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisitioncited by 8
  11. 2021A Scalable Gradient-Free Method for Bayesian Experimental Design with Implicit Modelscited by 7
  12. 2022Design Amortization for Bayesian Optimal Experimental Designcited by 7
  13. 2023Statistically Efficient Bayesian Sequential Experiment Design via Reinforcement Learning with Cross-Entropy Estimatorscited by 6
  14. 2023Bayesian Active Learning in the Presence of Nuisance Parameterscited by 6
  15. 2022Statistical applications of contrastive learningcited by 5
  16. 2023On Estimating the Gradient of the Expected Information Gain in Bayesian Experimental Designcited by 5
  17. 2024PASOA- PArticle baSed Bayesian Optimal Adaptive designcited by 5
  18. 2024Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spacescited by 5
  19. 2022When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Developmentcited by 4
  20. 2023CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Designcited by 4
  21. 2025JADAI: Jointly Amortizing Adaptive Design and Bayesian Inferencecited by 4
  22. 2021Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairnesscited by 3
  23. 2022Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisationcited by 3
  24. 2025Performance Comparisons of Reinforcement Learning Algorithms for Sequential Experimental Designcited by 3
  25. 2025A Geometric Approach to Optimal Experimental Designcited by 3
  26. 2024Nonlinear Bayesian optimal experimental design using logarithmic Sobolev inequalitiescited by 2
  27. 2025Optimizing Likelihoods via Mutual Information: Bridging Simulation-Based Inference and Bayesian Optimal Experimental Designcited by 2
  28. 2025Bayesian Experimental Design for Model Discrepancy Calibration: An Auto-Differentiable Ensemble Kalman Inversion Approachcited by 2
  29. 2026Loss-Driven Bayesian Active Learningcited by 2
  30. 2026Constrained Bayesian Experimental Design via Online Planningcited by 2
  31. 2026Efficient Adaptive Data Acquisition via Pretrained Belief Representationscited by 2
  32. 2026Amortising Bayesian Experimental Design for Sequential Information Gathering in LLMscited by 2
  33. 2023Stochastic Gradient Bayesian Optimal Experimental Designs for Simulation-based Inferencecited by 1
  34. 2025Online Bayesian Experimental Design for Partially Observed Dynamical Systemscited by 1
  35. 2026Supercharging Simulation-Based Inference for Bayesian Optimal Experimental Designcited by 1
  36. 2026Bayesian Experimental Design via Score Matchingcited by 1
  37. 2021A Hybrid Gradient Method to Designing Bayesian Experiments for Implicit Modelscited by 0
  38. 2022An Optimal Likelihood Free Method for Biological Model Selectioncited by 0

50 most cited of 58 All 58 (JSON)