vsOED uses a variational one-point reward and RL policy optimization to provide a lower bound on expected information gain for sequential experimental design, supporting nuisance parameters, implicit likelihoods, and multiple design goals.
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A constrained density-ratio network approximates the Radon-Nikodym derivative and feeds an anytime PAC-Bayes certificate for learning under covariate shift, validated via synthetic patch tests and real-data deployment.
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Variational Sequential Optimal Experimental Design using Reinforcement Learning
vsOED uses a variational one-point reward and RL policy optimization to provide a lower bound on expected information gain for sequential experimental design, supporting nuisance parameters, implicit likelihoods, and multiple design goals.
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Anytime PAC-Bayes for Constrained Density-Ratio Networks under Covariate Shift
A constrained density-ratio network approximates the Radon-Nikodym derivative and feeds an anytime PAC-Bayes certificate for learning under covariate shift, validated via synthetic patch tests and real-data deployment.