Framework combining constrained density-ratio networks with anytime PAC-Bayes for covariate shift.
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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
Framework combining constrained density-ratio networks with anytime PAC-Bayes for covariate shift.
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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.