124 citations · 528 across the 41 of their papers we have counts for
6 papers · 2 filters
Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures
Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt
Stein's method (Stein, 1973; 1981) is a powerful tool for statistical applications and has significantly impacted machine learning. Stein's lemma plays an essential role in Stein's…
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations
Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt
Natural-gradient methods enable fast and simple algorithms for variational inference, but due to computational difficulties, their use is mostly limited to \emph{minimal} exponenti…
Practical Deep Learning with Bayesian Principles
Kazuki Osawa, Siddharth Swaroop, Anirudh Jain +4
Bayesian methods promise to fix many shortcomings of deep learning, but they are impractical and rarely match the performance of standard methods, let alone improve them. In this p…
Approximate Inference Turns Deep Networks into Gaussian Processes
Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi +1
Deep neural networks (DNN) and Gaussian processes (GP) are two powerful models with several theoretical connections relating them, but the relationship between their training metho…
Scalable Training of Inference Networks for Gaussian-Process Models
Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu
Inference in Gaussian process (GP) models is computationally challenging for large data, and often difficult to approximate with a small number of inducing points. We explore an al…
A Generalization Bound for Online Variational Inference
Badr-Eddine Chérief-Abdellatif, Pierre Alquier, Mohammad Emtiyaz Khan
Bayesian inference provides an attractive online-learning framework to analyze sequential data, and offers generalization guarantees which hold even with model mismatch and adversa…