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Found 6 papers, 4 papers with code
Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference
We propose a method to improve the efficiency and accuracy of amortized Bayesian inference by leveraging universal symmetries in the joint probabilistic model of parameters and data.
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design
We formalize the problem of contextual optimization through the lens of Bayesian experimental design and propose CO-BED -- a general, model-agnostic framework for designing contextual experiments using information-theoretic principles.
Differentiable Multi-Target Causal Bayesian Experimental Design
We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting -- a critical component for causal discovery from finite data where interventions can be costly or risky.
Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation
In this paper we introduce a model-agnostic framework for gathering data to evaluate and improve contextual decision making through Bayesian Experimental Design.
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
We introduce implicit Deep Adaptive Design (iDAD), a new method for performing adaptive experiments in real-time with implicit models.
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design
We introduce Deep Adaptive Design (DAD), a method for amortizing the cost of adaptive Bayesian experimental design that allows experiments to be run in real-time.
