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Gia-Lac Tran

2 accepted papers

2021

Sparse within Sparse Gaussian Processes using Neighbor Information

ICML 2021spotlight

Approximations to Gaussian processes (GPs) based on inducing variables, combined with variational inference techniques, enable state-of-the-art sparse approaches to infer GPs at scale through mini-batch based learning. In this work, we further push the limits of scalability of sparse GPs by allowing…

Cited by 21Source ↗PDF ↗Scholar ↗
2019

Calibrating Deep Convolutional Gaussian Processes

AISTATS 2019poster

The wide adoption of Convolutional Neural Networks CNNs in applications where decision-making under uncertainty is fundamental, has brought a great deal of attention to the ability of these models to accurately quantify the uncertainty in their predictions. Previous work on combining CNNs with Gauss…

Cited by 54Source ↗PDF ↗Scholar ↗