AIConfPaper
← Search

Andrew Y. K. Foong

6 accepted papers

2026

Position: Agentic AI systems should be making Bayes-consistent decisions

Theodore Papamarkou, Pierre Alquier, Matthias Bauer, Wray Buntine, Andrew Davison, Gintare Karolina Dziugaite +24
ICML 2026poster

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 consult, or how many resources to invest. While the usefulness and feasibility of Bayesian approaches remain unclear for L…

Cited by 0Source ↗Scholar ↗
2023

Autoregressive Conditional Neural Processes

Wessel Bruinsma, Stratis Markou, James Requeima, Andrew Y. K. Foong, Tom Andersson, Anna Vaughan +3
ICLR 2023poster

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 likelihood procedure. Although CNPs have many advantages, they are unable to model dependencies in their predictions. Variou…

Cited by 30Source ↗PDF ↗code ↗Scholar ↗
2023

Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics

Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Kacper Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin +2
NeurIPS 2023spotlight

*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 timesteps on the order of femtoseconds ($1\textrm{fs}=10^{-15}\textrm{s}$). MD is often used to compute equilibrium p…

Cited by 46Source ↗PDF ↗code ↗Scholar ↗
2021

Collapsed Variational Bounds for Bayesian Neural Networks

Marcin B. Tomczak, Siddharth Swaroop, Andrew Y. K. Foong, Richard E Turner
NeurIPS 2021poster

Recent interest in learning large variational Bayesian Neural Networks (BNNs) has been partly hampered by poor predictive performance caused by underfitting, and their performance is known to be very sensitive to the prior over weights. Current practice often fixes the prior parameters to standard v…

Cited by 24Source ↗PDF ↗code ↗Scholar ↗
2021

How Tight Can PAC-Bayes be in the Small Data Regime?

Andrew Y. K. Foong, Wessel Bruinsma, David R. Burt, Richard E Turner
NeurIPS 2021poster

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, test set bounds adversely affect generalisation performance by withholding data from the training procedure. In this s…

Cited by 35Source ↗PDF ↗code ↗Scholar ↗
2020

Convolutional Conditional Neural Processes

Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner
ICLR 2020talk

We introduce the Convolutional Conditional Neural Process (ConvCNP), a new member of the Neural Process family that models translation equivariance in the data. Translation equivariance is an important inductive bias for many learning problems including time series modelling, spatial data, and image…

Cited by 196Source ↗code ↗Scholar ↗