42 citations · 128 across the 11 of their papers we have counts for
12 papers
Position: agentic AI orchestration should be Bayes-consistent
Theodore Papamarkou, Pierre Alquier, Matthias Bauer +27
LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to co…
Denoising Diffusion Probabilistic Models in Six Simple Steps
Richard E. Turner, Cristiana-Diana Diaconu, Stratis Markou +3
Denoising Diffusion Probabilistic Models (DDPMs) are a very popular class of deep generative model that have been successfully applied to a diverse range of problems including imag…
Autoregressive Conditional Neural Processes
Wessel P. Bruinsma, Stratis Markou, James Requiema +6
Conditional neural processes (CNPs; Garnelo et al., 2018a) are attractive meta-learning models which produce well-calibrated predictions and are trainable via a simple maximum like…
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde +5
Molecular dynamics (MD) simulation is a widely used technique to simulate molecular systems, most commonly at the all-atom resolution where equations of motion are integrated with…
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…
How Tight Can PAC-Bayes be in the Small Data Regime?
Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt +1
In this paper, we investigate the question: Given a small number of datapoints, for example N = 30, how tight can PAC-Bayes and test set bounds be made? For such small datasets, te…
The Gaussian Neural Process
Wessel P. Bruinsma, James Requeima, Andrew Y. K. Foong +2
Neural Processes (NPs; Garnelo et al., 2018a,b) are a rich class of models for meta-learning that map data sets directly to predictive stochastic processes. We provide a rigorous a…
Structured Weight Priors for Convolutional Neural Networks
Tim Pearce, Andrew Y. K. Foong, Alexandra Brintrup
Selection of an architectural prior well suited to a task (e.g. convolutions for image data) is crucial to the success of deep neural networks (NNs). Conversely, the weight priors…
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
Andrew Y. K. Foong, Wessel P. Bruinsma, Jonathan Gordon +3
Stationary stochastic processes (SPs) are a key component of many probabilistic models, such as those for off-the-grid spatio-temporal data. They enable the statistical symmetry of…
Convolutional Conditional Neural Processes
Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong +3
We introduce the Convolutional Conditional Neural Process (ConvCNP), a new member of the Neural Process family that models translation equivariance in the data. Translation equivar…
On the Expressiveness of Approximate Inference in Bayesian Neural Networks
Andrew Y. K. Foong, David R. Burt, Yingzhen Li +1
While Bayesian neural networks (BNNs) hold the promise of being flexible, well-calibrated statistical models, inference often requires approximations whose consequences are poorly…
'In-Between' Uncertainty in Bayesian Neural Networks
Andrew Y. K. Foong, Yingzhen Li, José Miguel Hernández-Lobato +1
We describe a limitation in the expressiveness of the predictive uncertainty estimate given by mean-field variational inference (MFVI), a popular approximate inference method for B…