19 citations · 51 across the 6 of their papers we have counts for
10 papers
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov +1
Aleatoric uncertainty captures the inherent randomness of the data, such as measurement noise. In Bayesian regression, we often use a Gaussian observation model, where we control t…
Conditioning Sparse Variational Gaussian Processes for Online Decision-making
Wesley J. Maddox, Samuel Stanton, Andrew Gordon Wilson
With a principled representation of uncertainty and closed form posterior updates, Gaussian processes (GPs) are a natural choice for online decision making. However, Gaussian proce…
Bayesian Optimization with High-Dimensional Outputs
Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson +1
Bayesian Optimization is a sample-efficient black-box optimization procedure that is typically applied to problems with a small number of independent objectives. However, in practi…
Fast Adaptation with Linearized Neural Networks
Wesley J. Maddox, Shuai Tang, Pablo Garcia Moreno +2
The inductive biases of trained neural networks are difficult to understand and, consequently, to adapt to new settings. We study the inductive biases of linearizations of neural n…
Kernel Interpolation for Scalable Online Gaussian Processes
Samuel Stanton, Wesley J. Maddox, Ian Delbridge +1
Gaussian processes (GPs) provide a gold standard for performance in online settings, such as sample-efficient control and black box optimization, where we need to update a posterio…
Similarity of Neural Networks with Gradients
Shuai Tang, Wesley J. Maddox, Charlie Dickens +2
A suitable similarity index for comparing learnt neural networks plays an important role in understanding the behaviour of the highly-nonlinear functions, and can provide insights…