Selected Publications

We show that function-space variational inference (FVI) between parametric NNs and non-parametric GPs cannot be done with the KL divergence. We test one famous FVI method (Sun et al., 2018) on Bayesian linear regression and find its approximation is far from the true posterior.
Submitted to AABI, 2020

We show that the output of a CNN or ResNet with an appropriate prior over the parameters is a Gaussian process in the limit of infinitely many convolutional filters.
Poster in ICLR, 2019

Recent Publications

We show that function-space variational inference (FVI) between parametric NNs and non-parametric GPs cannot be done with the KL …

We show that the output of a CNN or ResNet with an appropriate prior over the parameters is a Gaussian process in the limit of …

A method for filling in missing measurements in points in datasets, with some statistical assumptions, by drawing from the conditional …

How much domain knowledge do a SoTA planning algorithm, and a basic RL algorithm, need to solve Montezuma’s Revenge, the …

Projects

Engineering Safe AI reading group

I ran a reading group about AI safety in the Engineering department at Cambridge, from 2017 to 2019.

Malmö Collaborative AI Challenge

Entry to the Malmö Collaborative AI Challenge, got 1st and 3rd places in different categories.

Recent Posts

More Posts

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This is a writeup of the problems my team solved in the Murcia contest, which we were participating in as preparation for SWERC. Those …

The information in this post is taken from the sources listed in the Sources section at the end of it. If you don’t care about …

Recent & Upcoming Talks