A Multiscale Analysis of Diffusions on Rapidly Varying Surfaces. - Andrew B. Duncan; Charles M. Elliott; Grigorios A. Pavliotis; Andrew M. Stuart (J. Nonlinear Sci., 2015)

Homogenization of Lateral Diffusion on a Random Surface. - Andrew B. Duncan (Multiscale Model. Simul., 2015)

Measuring Sample Quality with Diffusions. - Jack Gorham; Andrew B. Duncan; Sebastian J. Vollmer; Lester W. Mackey (CoRR, 2016)

Hybrid framework for the simulation of stochastic chemical kinetics. - Andrew B. Duncan; Radek Erban; Konstantinos C. Zygalakis (J. Comput. Phys., 2016)

Minimum Stein Discrepancy Estimators. - Alessandro Barp; François-Xavier Briol; Andrew B. Duncan; Mark A. Girolami; Lester W. Mackey (NeurIPS, 2019)

Statistical Inference for Generative Models with Maximum Mean Discrepancy. - François-Xavier Briol; Alessandro Barp; Andrew B. Duncan; Mark A. Girolami (CoRR, 2019)

Scalable Control Variates for Monte Carlo Methods Via Stochastic Optimization. - Shijing Si; Chris J. Oates; Andrew B. Duncan; Lawrence Carin; François-Xavier Briol (MCQMC, 2020)

Blade Envelopes Part I: Concept and Methodology. - Chun Yui Wong; Pranay Seshadri; Ashley Scillitoe; Andrew B. Duncan; Geoffrey T. Parks (CoRR, 2020)

Bayesian Assessments of Aeroengine Performance. - Pranay Seshadri; Andrew B. Duncan; George Thorne; Geoffrey T. Parks; Mark Girolami (CoRR, 2020)

Blade Envelopes Part II: Multiple Objectives and Inverse Design. - Chun Yui Wong; Pranay Seshadri; Ashley Scillitoe; Bryn Noel Ubald; Andrew B. Duncan; Geoffrey T. Parks (CoRR, 2020)

Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods. - Andrew B. Duncan; Andrew M. Stuart; Marie-Therese Wolfram (CoRR, 2021)

Probabilistic Gradients for Fast Calibration of Differential Equation Models. - Jonathan Cockayne; Andrew B. Duncan (SIAM/ASA J. Uncertain. Quantification, 2021)

Grassmann Stein Variational Gradient Descent. - Xing Liu; Harrison Zhu; Jean-Francois Ton; George Wynne; Andrew B. Duncan (AISTATS, 2022)

Density Estimation from Schlieren Images through Machine Learning. - Bryn Noel Ubald; Pranay Seshadri; Andrew B. Duncan (CoRR, 2022)

F-EBM: Energy Based Learning of Functional Data. - Jen Ning Lim; Sebastian J. Vollmer; Lorenz Wolf; Andrew B. Duncan (CoRR, 2022)

Prior-informed Uncertainty Modelling with Bayesian Polynomial Approximations. - Chun Yui Wong; Pranay Seshadri; Andrew B. Duncan; Ashley Scillitoe; Geoffrey T. Parks (CoRR, 2022)

Knowledge Transfer in Engineering Fleets: Hierarchical Bayesian Modelling for Multi-Task Learning. - Lawrence A. Bull; Maharshi Harshadbhai Dhada; Olof Steinert; Tony Lindgren; Ajith Kumar Parlikad; Andrew B. Duncan; Mark Girolami (CoRR, 2022)

Batch Bayesian Optimization via Particle Gradient Flows. - Enrico Crovini; Simon L. Cotter; Konstantinos Zygalakis; Andrew B. Duncan (CoRR, 2022)

A Kernel Two-Sample Test for Functional Data. - George Wynne; Andrew B. Duncan (J. Mach. Learn. Res., 2022)

Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods. - Oliver R. A. Dunbar; Andrew B. Duncan; Andrew M. Stuart; Marie-Therese Wolfram (SIAM J. Appl. Dyn. Syst., 2022)

Energy-Based Models for Functional Data using Path Measure Tilting. - Jen Ning Lim; Sebastian J. Vollmer; Lorenz Wolf; Andrew B. Duncan (AISTATS, 2023)

A High-dimensional Convergence Theorem for U-statistics with Applications to Kernel-based Testing. - Kevin Han Huang; Xing Liu; Andrew B. Duncan; Axel Gandy (COLT, 2023)

Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy. - Xing Liu; Andrew B. Duncan; Axel Gandy (ICML, 2023)

Energy Discrepancies: A Score-Independent Loss for Energy-Based Models. - Tobias Schröder; Zijing Ou; Jen Lim; Yingzhen Li; Sebastian J. Vollmer; Andrew B. Duncan (NeurIPS, 2023)

Hierarchical Bayesian modeling for knowledge transfer across engineering fleets via multitask learning. - Lawrence A. Bull; Domenic Di Francesco; Maharshi Harshadbhai Dhada; Olof Steinert; Tony Lindgren; Ajith Kumar Parlikad; Andrew B. Duncan; Mark Girolami (Comput. Aided Civ. Infrastructure Eng., 2023)

Encoding Domain Expertise into Multilevel Models for Source Location. - Lawrence A. Bull; Matthew R. Jones; Elizabeth J. Cross; Andrew B. Duncan; Mark Girolami (CoRR, 2023)

Energy Discrepancies: A Score-Independent Loss for Energy-Based Models. - Tobias Schröder; Zijing Ou; Jen Ning Lim; Yingzhen Li; Sebastian J. Vollmer; Andrew B. Duncan (CoRR, 2023)

Training Discrete Energy-Based Models with Energy Discrepancy. - Tobias Schröder; Zijing Ou; Yingzhen Li; Andrew B. Duncan (CoRR, 2023)

On the geometry of Stein variational gradient descent. - Andrew B. Duncan; Nikolas Nüsken; Lukasz Szpruch (J. Mach. Learn. Res., 2023)

Theoretical Guarantees for the Statistical Finite Element Method. - Yanni Papandreou; Jon Cockayne; Mark Girolami; Andrew B. Duncan (SIAM/ASA J. Uncertain. Quantification, 2023)

Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces. - Tobias Schröder; Zijing Ou; Yingzhen Li; Andrew B. Duncan (NeurIPS, 2024)

Modelling Global Trade with Optimal Transport. - Thomas Gaskin; Marie-Therese Wolfram; Andrew B. Duncan; Güven Demirel (CoRR, 2024)

Deep Optimal Sensor Placement for Black Box Stochastic Simulations. - Paula Cordero-Encinar; Tobias Schröder; Peter Yatsyshin; Andrew B. Duncan (AISTATS, 2025)

Non-asymptotic Analysis of Diffusion Annealed Langevin Monte Carlo for Generative Modelling. - Paula Cordero-Encinar; Ömer Deniz Akyildiz; Andrew B. Duncan (CoRR, 2025)

Physics-Constrained Fine-Tuning of Flow-Matching Models for Generation and Inverse Problems. - Jan Tauberschmidt; Sophie Fellenz; Sebastian J. Vollmer; Andrew B. Duncan (CoRR, 2025)

Retrieval-augmented reasoning with lean language models. - Ryan Sze-Yin Chan; Federico Nanni; Tomas Lazauskas; Rosie Wood; Penelope Yong; Lionel Tarassenko; Mark Girolami; James Geddes; Andrew B. Duncan (CoRR, 2025)