8 citations · 11 across the 14 of their papers we have counts for
6 papers · 1 filter
Stochastic Gradient Descent for Gaussian Processes Done Right
Jihao Andreas Lin, Shreyas Padhy, Javier Antorán +5
As is well known, both sampling from the posterior and computing the mean of the posterior in Gaussian process regression reduces to solving a large linear system of equations. We…
Beyond Intuition, a Framework for Applying GPs to Real-World Data
Kenza Tazi, Jihao Andreas Lin, Ross Viljoen +4
Gaussian Processes (GPs) offer an attractive method for regression over small, structured and correlated datasets. However, their deployment is hindered by computational costs and…
Function-Space Regularization for Deep Bayesian Classification
Jihao Andreas Lin, Joe Watson, Pascal Klink +1
Bayesian deep learning approaches assume model parameters to be latent random variables and infer posterior distributions to quantify uncertainty, increase safety and trust, and pr…
Minimal Random Code Learning with Mean-KL Parameterization
Jihao Andreas Lin, Gergely Flamich, José Miguel Hernández-Lobato
This paper studies the qualitative behavior and robustness of two variants of Minimal Random Code Learning (MIRACLE) used to compress variational Bayesian neural networks. MIRACLE…
Online Laplace Model Selection Revisited
Jihao Andreas Lin, Javier Antorán, José Miguel Hernández-Lobato
The Laplace approximation provides a closed-form model selection objective for neural networks (NN). Online variants, which optimise NN parameters jointly with hyperparameters, lik…
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
Jihao Andreas Lin, Javier Antorán, Shreyas Padhy +3
Gaussian processes are a powerful framework for quantifying uncertainty and for sequential decision-making but are limited by the requirement of solving linear systems. In general,…