24 citations · 168 across the 34 of their papers we have counts for
6 papers · 1 filter
Variational Laplace for Bayesian neural networks
Ali Unlu, Laurence Aitchison
We develop variational Laplace for Bayesian neural networks (BNNs) which exploits a local approximation of the curvature of the likelihood to estimate the ELBO without the need for…
Deep kernel processes
Laurence Aitchison, Adam X. Yang, Sebastian W. Ober
We define deep kernel processes in which positive definite Gram matrices are progressively transformed by nonlinear kernel functions and by sampling from (inverse) Wishart distribu…
Legally grounded fairness objectives
Dylan Holden-Sim, Gavin Leech, Laurence Aitchison
Recent work has identified a number of formally incompatible operational measures for the unfairness of a machine learning (ML) system. As these measures all capture intuitively de…
Semi-supervised learning objectives as log-likelihoods in a generative model of data curation
Stoil Ganev, Laurence Aitchison
We currently do not have an understanding of semi-supervised learning (SSL) objectives such as pseudo-labelling and entropy minimization as log-likelihoods, which precludes the dev…
A statistical theory of cold posteriors in deep neural networks
Laurence Aitchison
To get Bayesian neural networks to perform comparably to standard neural networks it is usually necessary to artificially reduce uncertainty using a "tempered" or "cold" posterior.…
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes
Sebastian W. Ober, Laurence Aitchison
We consider the optimal approximate posterior over the top-layer weights in a Bayesian neural network for regression, and show that it exhibits strong dependencies on the lower-lay…