The framework proposed by the authors integrates uncertainty-aware mechanisms at multiple stages of the LLM lifecycle. It ...

Assistant professor Sean Plummer was part of an international team that organized a March workshop on Bayesian Interference, which drew participants from around the globe to the Banff International ...

Bayesian Inference: Bayes theorem, prior, posterior and predictive distributions, conjugate models (Normal-Normal, Poisson-Gamma, Beta-Binomial), Bayesian point estimation, credible intervals and ...

The suggested augmentation robustifies the baseline parametric model to local misspecification, while preserving the appeal of Bayesian inference. We develop an MCMC algorithm for the augmented model ...

We develop new methods and algorithms for coping with uncertainty in artificial intelligence, focusing in particular on approximate Bayesian inference of probabilistic programs. We also solve ...