6 citations · 21 across the 6 of their papers we have counts for
5 papers · 1 filter
Scaling Up Bayesian Neural Networks with Neural Networks
Zahra Moslemi, Yang Meng, Shiwei Lan +1
Bayesian Neural Networks (BNNs) offer a principled and natural framework for proper uncertainty quantification in the context of deep learning. They address the typical challenges…
Calibrate, Emulate, Sample
Emmet Cleary, Alfredo Garbuno-Inigo, Shiwei Lan +2
Many parameter estimation problems arising in applications are best cast in the framework of Bayesian inversion. This allows not only for an estimate of the parameters, but also fo…
Deep Markov Chain Monte Carlo
Babak Shahbaba, Luis Martinez Lomeli, Tian Chen +1
We propose a new computationally efficient sampling scheme for Bayesian inference involving high dimensional probability distributions. Our method maps the original parameter space…
Adaptive Dimension Reduction to Accelerate Infinite-Dimensional Geometric Markov Chain Monte Carlo
Shiwei Lan
Bayesian inverse problems highly rely on efficient and effective inference methods for uncertainty quantification (UQ). Infinite-dimensional MCMC algorithms, directly defined on fu…
Sampling constrained probability distributions using Spherical Augmentation
Shiwei Lan, Babak Shahbaba
Statistical models with constrained probability distributions are abundant in machine learning. Some examples include regression models with norm constraints (e.g., Lasso), probit,…