1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2023
Generative Posterior Networks for Approximately Bayesian Epistemic Uncertainty Estimation
Melrose Roderick, Felix Berkenkamp, Fatemeh Sheikholeslami +1
In many real-world problems, there is a limited set of training data, but an abundance of unlabeled data. We propose a new method, Generative Posterior Networks (GPNs), that uses u…
cs.LG2023★ 1 cited
Model-Based Uncertainty in Value Functions
Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska +2
We consider the problem of quantifying uncertainty over expected cumulative rewards in model-based reinforcement learning. In particular, we focus on characterizing the variance ov…