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Generative Posterior Networks for Approximately Bayesian Epistemic Uncertainty Estimation
Melrose Roderick · Felix Berkenkamp · Fatemeh Sheikholeslami · J. Zico Kolter

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in Workshop on Distribution Shifts: Connecting Methods and Applications »
Event URL: https://openreview.net/forum?id=R0fEp08mUo »

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 unlabeled data to estimate epistemic uncertainty in high-dimensional problems. A GPN is a generative model that, given a prior distribution over functions, approximates the posterior distribution directly by regularizing the network towards samples from the prior. We prove theoretically that our method indeed approximates the Bayesian posterior and show empirically that it improves epistemic uncertainty estimation and scalability over competing methods.

Author Information

Melrose Roderick (Carnegie Mellon University)
Felix Berkenkamp (Bosch Center for AI)
Fatemeh Sheikholeslami
J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

Zico Kolter is an Assistant Professor in the School of Computer Science at Carnegie Mellon University, and also serves as Chief Scientist of AI Research for the Bosch Center for Artificial Intelligence. His work focuses on the intersection of machine learning and optimization, with a large focus on developing more robust, explainable, and rigorous methods in deep learning. In addition, he has worked on a number of application areas, highlighted by work on sustainability and smart energy systems. He is the recipient of the DARPA Young Faculty Award, and best paper awards at KDD, IJCAI, and PESGM.

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