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 ...

Enhanced computing performance is achieved using nanomaterial-based probabilistic-bits with high operational stability.

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

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

Why are some patients hard to diagnose? Surely you can just run every test - doctors refuse to because they don't want to spend money, right?! Not quite. A millionaire patient asked me; if he was ...

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 ...

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 ...