124 citations · 528 across the 41 of their papers we have counts for
6 papers · 2 filters
Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression
Ayush Jain, P. K. Srijith, Mohammad Emtiyaz Khan
Deep Gaussian Processes (DGPs) are multi-layer, flexible extensions of Gaussian processes but their training remains challenging. Sparse approximations simplify the training but of…
Structured second-order methods via natural gradient descent
Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan +1
In this paper, we propose new structured second-order methods and structured adaptive-gradient methods obtained by performing natural-gradient descent on structured parameter space…
The Bayesian Learning Rule
Mohammad Emtiyaz Khan, Håvard Rue
We show that many machine-learning algorithms are specific instances of a single algorithm called the \emph{Bayesian learning rule}. The rule, derived from Bayesian principles, yie…
Bridging the Gap Between Target Networks and Functional Regularization
Alexandre Piché, Valentin Thomas, Rafael Pardinas +4
Bootstrapping is behind much of the successes of deep Reinforcement Learning. However, learning the value function via bootstrapping often leads to unstable training due to fast-ch…
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
Alexander Immer, Matthias Bauer, Vincent Fortuin +2
Marginal-likelihood based model-selection, even though promising, is rarely used in deep learning due to estimation difficulties. Instead, most approaches rely on validation data,…
Tractable structured natural gradient descent using local parameterizations
Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan +1
Natural-gradient descent (NGD) on structured parameter spaces (e.g., low-rank covariances) is computationally challenging due to difficult Fisher-matrix computations. We address th…