6 citations · 6 across the 10 of their papers we have counts for
10 papers
Constraining the lives and times of exoplanets through evolutionary Bayesian retrievals
Harrison Nicholls, Tim Lichtenberg, Ben Riegler +2
Static retrieval frameworks are leading tools for interpreting exoplanet observations, yet time-independent modelling leaves them prone to degeneracy and unable to resolve exoplane…
Decision-Aligned Evaluation of Uncertainty Quantification
Annika Schneider, Tommy Rochussen, Joshua Stiller +1
Uncertainty estimates in machine learning are typically evaluated using generic metrics such as the negative log-likelihood and expected calibration error, yet good performance on…
Gaussian Mean Field Variational Inference can Overestimate Predictive Variance
James Odgers, Ben Riegler, Siddharth Swaroop +1
Mean Field Variational Inference (MFVI) is widely understood to underestimate posterior variance. By analysing conjugate Bayesian Linear Regression (BLR), we show that this charact…
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…
Standard Acquisition Is Sufficient for Asynchronous Bayesian Optimization
Ben Riegler, James Odgers, Vincent Fortuin
Asynchronous Bayesian optimization is widely used for gradient-free optimization in domains with independent parallel experiments and varying evaluation times. Existing methods pos…
In-Context Function Learning in Large Language Models
Elif Akata, Konstantinos Voudouris, Vincent Fortuin +1
Large language models (LLMs) can learn from a few demonstrations provided at inference time. We study this in-context learning phenomenon through the lens of Gaussian Processes (GP…
Amortising Inference and Meta-Learning Priors in Neural Networks
Tommy Rochussen, Vincent Fortuin
One of the core facets of Bayesianism is in the updating of prior beliefs in light of new evidenceso how can we maintain a Bayesian approach if we have no prior belief…
On the Effect of Regularization on Nonparametric Mean-Variance Regression
Eliot Wong-Toi, Alex Boyd, Vincent Fortuin +1
Uncertainty quantification is vital for decision-making and risk assessment in machine learning. Mean-variance regression models, which predict both a mean and residual noise for e…
Beyond Quantification: Navigating Uncertainty in Professional AI Systems
Sylvie Delacroix, Diana Robinson, Umang Bhatt +12
The growing integration of large language models across professional domains transforms how experts make critical decisions in healthcare, education, and law. While significant res…
Sparse Gaussian Neural Processes
Tommy Rochussen, Vincent Fortuin
Despite significant recent advances in probabilistic meta-learning, it is common for practitioners to avoid using deep learning models due to a comparative lack of interpretability…