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This paper formulates cognitive experiment design as a Bayesian Experimental Design problem, introducing an amortized framework that efficiently identifies maximally informative environments for inferring latent planning parameters, with validated performance on the Mouselab-MDP paradigm.
This paper presents a hybrid amortized inference method that accelerates hierarchical sparse predictive coding by combining a fast initial estimate with corrective refinement steps, achieving better efficiency than pure iterative or amortized approaches.
Introduces Amortized Factor Inference Networks (AFINs), a family of encode-merge-decode inference networks that generalize across varying priors, likelihoods, and dimensionality, achieving posterior accuracy comparable to NUTS with much less compute.
SurvivalPFN is a prior-data fitted network that amortizes Bayesian inference for survival analysis via in-context learning, achieving strong predictive performance across 61 datasets without task-specific training or hyperparameter tuning.