55 citations · 107 across the 12 of their papers we have counts for
12 papers · 1 filter
Locally Smoothed Gaussian Process Regression
Davit Gogolashvili, Bogdan Kozyrskiy, Maurizio Filippone
We develop a novel framework to accelerate Gaussian process regression (GPR). In particular, we consider localization kernels at each data point to down-weigh the contributions fro…
Local Random Feature Approximations of the Gaussian Kernel
Jonas Wacker, Maurizio Filippone
A fundamental drawback of kernel-based statistical models is their limited scalability to large data sets, which requires resorting to approximations. In this work, we focus on the…
Model Selection for Bayesian Autoencoders
Ba-Hien Tran, Simone Rossi, Dimitrios Milios +3
We develop a novel method for carrying out model selection for Bayesian autoencoders (BAEs) by means of prior hyper-parameter optimization. Inspired by the common practice of type-…
Sparse within Sparse Gaussian Processes using Neighbor Information
Gia-Lac Tran, Dimitrios Milios, Pietro Michiardi +1
Approximations to Gaussian processes based on inducing variables, combined with variational inference techniques, enable state-of-the-art sparse approaches to infer GPs at scale th…
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations
Simone Rossi, Markus Heinonen, Edwin V. Bonilla +2
Variational inference techniques based on inducing variables provide an elegant framework for scalable posterior estimation in Gaussian process (GP) models. Besides enabling scalab…
Efficient Approximate Inference with Walsh-Hadamard Variational Inference
Simone Rossi, Sebastien Marmin, Maurizio Filippone
Variational inference offers scalable and flexible tools to tackle intractable Bayesian inference of modern statistical models like Bayesian neural networks and Gaussian processes.…