mlsanityData to 7 Oct 2026

Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods

arXiv 2111.02329 ↗First posted 3 Nov 2021stat.ML cs.AI cs.LGcited by 61

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Abstract (arXiv)

We introduce implicit Deep Adaptive Design (iDAD), a new method for performing adaptive experiments in real-time with implicit models. iDAD amortizes the cost of Bayesian optimal experimental design (BOED) by learning a design policy network upfront, which can then be deployed quickly at the time of the experiment. The iDAD network can be trained on any model which simulates differentiable samples, unlike previous design policy work that requires a closed form likelihood and conditionally independent experiments. At deployment, iDAD allows design decisions to be made in milliseconds, in contrast to traditional BOED approaches that require heavy computation during the experiment itself. We illustrate the applicability of iDAD on a number of experiments, and show that it provides a fast and effective mechanism for performing adaptive design with implicit models.

Key facts. Cited by 61 papers in the map (data through 7 Oct 2026). Builds on 32 of its 62 references that are in the map. First posted on arXiv 3 Nov 2021.

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32 of 62 references.

References · 32

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Citing papers per year: 2022: 8; 2023: 12; 2024: 10; 2025: 14; 2026: 17.

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  1. 2023Modern Bayesian Experimental Designcited by 121
  2. 2023GFlowNets for AI-Driven Scientific Discoverycited by 63
  3. 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
  4. 2022Optimizing Sequential Experimental Design with Deep Reinforcement Learningcited by 45
  5. 2023JANA: Jointly Amortized Neural Approximation of Complex Bayesian Modelscited by 41
  6. 2025BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Designcited by 21
  7. 2024Amortized Bayesian Experimental Design for Decision-Makingcited by 20
  8. 2022Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Modelscited by 15
  9. 2023Differentiable Multi-Target Causal Bayesian Experimental Designcited by 15
  10. 2024Variational Bayesian Optimal Experimental Design with Normalizing Flowscited by 13
  11. 2024Nesting Particle Filters for Experimental Design in Dynamical Systemscited by 12
  12. 2025Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Designcited by 12
38 more
  1. 2024Amortized Active Causal Induction with Deep Reinforcement Learningcited by 10
  2. 2024Bayesian Experimental Design via Contrastive Diffusionscited by 9
  3. 2023Variational Sequential Optimal Experimental Design using Reinforcement Learningcited by 8
  4. 2025ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisitioncited by 8
  5. 2025PABBO: Preferential Amortized Black-Box Optimizationcited by 7
  6. 2022Statistical applications of contrastive learningcited by 5
  7. 2023Statistically Efficient Bayesian Sequential Experiment Design via Reinforcement Learning with Cross-Entropy Estimatorscited by 5
  8. 2023Bayesian Active Learning in the Presence of Nuisance Parameterscited by 5
  9. 2024PASOA- PArticle baSed Bayesian Optimal Adaptive designcited by 5
  10. 2025Active Learning of Model Discrepancy with Bayesian Experimental Designcited by 5
  11. 2022Bayesian Active Learning for Discrete Latent Variable Modelscited by 4
  12. 2023CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Designcited by 4
  13. 2022Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisationcited by 3
  14. 2023Online simulator-based experimental design for cognitive model selectioncited by 3
  15. 2023Experimentation Platforms Meet Reinforcement Learning: Bayesian Sequential Decision-Making for Continuous Monitoringcited by 3
  16. 2023Designing Optimal Behavioral Experiments Using Machine Learningcited by 3
  17. 2024Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Designcited by 3
  18. 2025Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies From Simulated Nonparametric Functionscited by 3
  19. 2025Performance Comparisons of Reinforcement Learning Algorithms for Sequential Experimental Designcited by 3
  20. 2022Actively learning to learn causal relationshipscited by 2
  21. 2024A Likelihood-Free Approach to Goal-Oriented Bayesian Optimal Experimental Designcited by 2
  22. 2025Optimizing Likelihoods via Mutual Information: Bridging Simulation-Based Inference and Bayesian Optimal Experimental Designcited by 2
  23. 2025PIED: Physics-Informed Experimental Design for Inverse Problemscited by 2
  24. 2025A Geometric Approach to Optimal Experimental Designcited by 2
  25. 2025JADAI: Jointly Amortizing Adaptive Design and Bayesian Inferencecited by 2
  26. 2026Constrained Bayesian Experimental Design via Online Planningcited by 2
  27. 2026Efficient Adaptive Data Acquisition via Pretrained Belief Representationscited by 2
  28. 2023Amortised Experimental Design and Parameter Estimation for User Models of Pointingcited by 1
  29. 2025Goal-Oriented Sequential Bayesian Experimental Design for Causal Learningcited by 1
  30. 2025Online Bayesian Experimental Design for Partially Observed Dynamical Systemscited by 1
  31. 2026Sequential Bayesian Optimal Experimental Design in Infinite Dimensions via Policy Gradient Reinforcement Learningcited by 1
  32. 2026Supercharging Simulation-Based Inference for Bayesian Optimal Experimental Designcited by 1
  33. 2026Deep Adaptive Model-Based Design of Experimentscited by 1
  34. 2026Conditional Neural Bayes Ratio Estimation for Experimental Design Optimisationcited by 1
  35. 2022Experimental Design for Multi-Channel Imaging via Task-Driven Feature Selectioncited by 0
  36. 2024Amortized Active Learning for Nonparametric Functionscited by 0
  37. 2024Mutual Information Multinomial Estimationcited by 0
  38. 2025Direct Regret Optimization in Bayesian Optimizationcited by 0

50 most cited of 61 All 61 (JSON)