36 citations · 138 across the 24 of their papers we have counts for
7 papers · 1 filter
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt +1
The kernel function and its hyperparameters are the central model selection choice in a Gaussian proces (Rasmussen and Williams, 2006). Typically, the hyperparameters of the kernel…
Environmental Sensor Placement with Convolutional Gaussian Neural Processes
Tom R. Andersson, Wessel P. Bruinsma, Stratis Markou +8
Environmental sensors are crucial for monitoring weather conditions and the impacts of climate change. However, it is challenging to place sensors in a way that maximises the infor…
Challenges and Pitfalls of Bayesian Unlearning
Ambrish Rawat, James Requeima, Wessel Bruinsma +1
Machine unlearning refers to the task of removing a subset of training data, thereby removing its contributions to a trained model. Approximate unlearning are one class of methods…
A Note on the Chernoff Bound for Random Variables in the Unit Interval
Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt
The Chernoff bound is a well-known tool for obtaining a high probability bound on the expectation of a Bernoulli random variable in terms of its sample average. This bound is commo…
Modelling Non-Smooth Signals with Complex Spectral Structure
Wessel P. Bruinsma, Martin Tegnér, Richard E. Turner
The Gaussian Process Convolution Model (GPCM; Tobar et al., 2015a) is a model for signals with complex spectral structure. A significant limitation of the GPCM is that it assumes a…
Practical Conditional Neural Processes Via Tractable Dependent Predictions
Stratis Markou, James Requeima, Wessel P. Bruinsma +2
Conditional Neural Processes (CNPs; Garnelo et al., 2018a) are meta-learning models which leverage the flexibility of deep learning to produce well-calibrated predictions and natur…
Wide Mean-Field Bayesian Neural Networks Ignore the Data
Beau Coker, Wessel P. Bruinsma, David R. Burt +2
Bayesian neural networks (BNNs) combine the expressive power of deep learning with the advantages of Bayesian formalism. In recent years, the analysis of wide, deep BNNs has provid…